diff options
| author | Clyne Sullivan <clyne@bitgloo.com> | 2025-02-02 11:26:53 -0500 |
|---|---|---|
| committer | Clyne Sullivan <clyne@bitgloo.com> | 2025-02-02 11:26:53 -0500 |
| commit | 9c59a184dba820975e5da6fcd5d248aee87f7e2f (patch) | |
| tree | 6b30516adc2ba0f7b0a8f5fb5d2e6966c03108d8 /Drivers/CMSIS/NN/Include | |
| parent | d09f4289b5788d6a8b34e424841292e2b8529e56 (diff) | |
add l476 implementationl476
Diffstat (limited to 'Drivers/CMSIS/NN/Include')
| -rw-r--r-- | Drivers/CMSIS/NN/Include/arm_nn_tables.h | 112 | ||||
| -rw-r--r-- | Drivers/CMSIS/NN/Include/arm_nnfunctions.h | 3607 | ||||
| -rw-r--r-- | Drivers/CMSIS/NN/Include/arm_nnsupportfunctions.h | 1455 |
3 files changed, 1400 insertions, 3774 deletions
diff --git a/Drivers/CMSIS/NN/Include/arm_nn_tables.h b/Drivers/CMSIS/NN/Include/arm_nn_tables.h index 327294d..973602b 100644 --- a/Drivers/CMSIS/NN/Include/arm_nn_tables.h +++ b/Drivers/CMSIS/NN/Include/arm_nn_tables.h @@ -1,56 +1,56 @@ -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_nn_tables.h - * Description: Extern declaration for NN tables - * - * $Date: 17. August 2021 - * $Revision: V.1.0.2 - * - * Target Processor: Cortex-M cores - * -------------------------------------------------------------------- */ -/* - * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef _ARM_NN_TABLES_H -#define _ARM_NN_TABLES_H - -#include "arm_nn_math_types.h" - -/** - * @brief tables for various activation functions - * - */ - -extern const q15_t sigmoidTable_q15[256]; -extern const q7_t sigmoidTable_q7[256]; - -extern const q7_t tanhTable_q7[256]; -extern const q15_t tanhTable_q15[256]; - -/** - * @brief 2-way tables for various activation functions - * - * 2-way table, H table for value larger than 1/4 - * L table for value smaller than 1/4, H table for remaining - * We have this only for the q15_t version. It does not make - * sense to have it for q7_t type - */ -extern const q15_t sigmoidHTable_q15[192]; -extern const q15_t sigmoidLTable_q15[128]; - -#endif /* ARM_NN_TABLES_H */ +/* ----------------------------------------------------------------------
+ * Project: CMSIS NN Library
+ * Title: arm_nn_tables.h
+ * Description: Extern declaration for NN tables
+ *
+ * $Date: 17. January 2018
+ * $Revision: V.1.0.0
+ *
+ * Target Processor: Cortex-M cores
+ * -------------------------------------------------------------------- */
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef _ARM_NN_TABLES_H
+#define _ARM_NN_TABLES_H
+
+#include "arm_math.h"
+
+/**
+* @brief tables for various activation functions
+*
+*/
+
+extern const q15_t sigmoidTable_q15[256];
+extern const q7_t sigmoidTable_q7[256];
+
+extern const q7_t tanhTable_q7[256];
+extern const q15_t tanhTable_q15[256];
+
+ /**
+ * @brief 2-way tables for various activation functions
+ *
+ * 2-way table, H table for value larger than 1/4
+ * L table for value smaller than 1/4, H table for remaining
+ * We have this only for the q15_t version. It does not make
+ * sense to have it for q7_t type
+ */
+extern const q15_t sigmoidHTable_q15[192];
+extern const q15_t sigmoidLTable_q15[128];
+
+#endif /* ARM_NN_TABLES_H */
diff --git a/Drivers/CMSIS/NN/Include/arm_nnfunctions.h b/Drivers/CMSIS/NN/Include/arm_nnfunctions.h index deaade7..eccbb41 100644 --- a/Drivers/CMSIS/NN/Include/arm_nnfunctions.h +++ b/Drivers/CMSIS/NN/Include/arm_nnfunctions.h @@ -1,2532 +1,1075 @@ -/* - * Copyright (C) 2010-2022 Arm Limited or its affiliates. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_nnfunctions.h - * Description: Public header file for CMSIS NN Library - * - * $Date: 19 April 2022 - * $Revision: V.9.0.0 - * - * Target Processor: Cortex-M CPUs - * -------------------------------------------------------------------- */ - -/** - \mainpage CMSIS NN Software Library - * - * Introduction - * ------------ - * - * This user manual describes the CMSIS NN software library, - * a collection of efficient neural network kernels developed to maximize the - * performance and minimize the memory footprint of neural networks on Cortex-M processor cores. - * - * The library is divided into a number of functions each covering a specific category: - * - Convolution Functions - * - Activation Functions - * - Fully-connected Layer Functions - * - SVDF Layer Functions - * - Pooling Functions - * - Softmax Functions - * - Basic math Functions - * - * The library has separate functions for operating on different weight and activation data - * types including 8-bit integers (q7_t) and 16-bit integers (q15_t). The descrition of the - * kernels are included in the function description. The implementation details are also - * described in this paper [1]. - * - * Supported Processors - * ------- - * CMSIS-NN targets Cortex-M processors with typically three different implementations for each function. Each - * targets a different group of processors. - * - Processors without SIMD capability (e.g, Cortex-M0) - * - Processors with DSP extention (e.g Cortex-M4) - * - Processors with MVE extension (e.g Cortex-M55) - * The right implementation is picked through feature flags and the user usually does not have to explicit set it. - * - * Function Classification - * -------- - * The functions can be classified into two segments - * - Legacy functions supporting ARM's internal symmetric quantization(8 bits). - * - Functions that support TensorFlow Lite framework with symmetric quantization(8 bits). - * - * The legacy functions can be identified with their suffix of _q7 or _q15 and are no new development is done there. - * The article in [2] describes in detail how to run a network using the legacy functions. - * - * The functions supporting TensorFlow Lite framework is identified by the _s8 suffix and can be invoked from TFL - * micro. The functions are bit exact to TensorFlow Lite. Refer to the TensorFlow's documentation in [3] on how to run - * a TensorFlow Lite model using optimized CMSIS-NN kernels. - * - * Block Diagram - * -------- - * \image html CMSIS-NN-OVERVIEW.PNG - * - * Examples - * -------- - * - * The library ships with a number of examples which demonstrate how to use the library functions. - * - * Pre-processor Macros - * ------------ - * - * Each library project have different pre-processor macros. - * - * - ARM_MATH_DSP: - * - * Define macro ARM_MATH_DSP, If the silicon supports DSP instructions(DSP extension). - * - * - ARM_MATH_MVEI: - * - * Define macro ARM_MATH_MVEI, If the silicon supports M-Profile Vector Extension. - - * - ARM_MATH_AUTOVECTORIZE - * Used in conjucture with ARM_MATH_MVEI to let the compiler auto vectorize for the functions that uses inline - * assembly. It does not affect functions that use C or intrinsics. - * - ARM_MATH_BIG_ENDIAN: - * - * Define macro ARM_MATH_BIG_ENDIAN to build the library for big endian targets. This is supported only for the legacy - * functions i.e, functions targetted at TensorFlow Lite do not support big endianness. By default library builds for - * little endian targets. - * - * - ARM_NN_TRUNCATE: - * - * Define macro ARM_NN_TRUNCATE to use floor instead of round-to-the-nearest-int for the computation. - * - * - * Copyright Notice - * ------------ - * - * Copyright (C) 2010-2019 Arm Limited. All rights reserved. - * - * [1] CMSIS-NN: Efficient Neural Network Kernels for Arm Cortex-M CPUs https://arxiv.org/abs/1801.06601 - * - * [2] Converting a Neural Network for Arm Cortex-M with CMSIS-NN - * - https://developer.arm.com/solutions/machine-learning-on-arm/developer-material/how-to-guides/converting-a-neural-network-for-arm-cortex-m-with-cmsis-nn/single-page - * [3] https://www.tensorflow.org/lite/microcontrollers/library - * - * [4] https://github.com/ARM-software/CMSIS_5/tree/develop/CMSIS/NN#legacy-vs-tfl-micro-compliant-apis - */ - -/** - * @defgroup groupNN Neural Network Functions - * A collection of functions to perform basic operations for neural network layers. Functions with a _s8 suffix support - * TensorFlow Lite framework. - */ - -#ifndef _ARM_NNFUNCTIONS_H -#define _ARM_NNFUNCTIONS_H - -#include "arm_nn_math_types.h" -#include "arm_nn_types.h" - -#define USE_INTRINSIC - -//#define ARM_NN_TRUNCATE /* This config the rounding model to floor or round to the nearest int */ - -#ifdef __cplusplus -extern "C" { -#endif - -/** - * @brief Struct for specifying activation function types - * - */ -typedef enum -{ - ARM_SIGMOID = 0, - /**< Sigmoid activation function */ - ARM_TANH = 1, - /**< Tanh activation function */ -} arm_nn_activation_type; - -/** - * @defgroup NNConv Convolution Functions - * - * Collection of convolution, depthwise convolution functions and their variants. - * - * The convolution is implemented in 2 steps: im2col and GEMM - * - * im2col is a process of converting each patch of image data into - * a column. After im2col, the convolution is computed as matrix-matrix - * multiplication. - * - * To reduce the memory footprint, the im2col is performed partially. - * Each iteration, only a few column (i.e., patches) are generated and - * computed with GEMM kernels similar to CMSIS-DSP arm_mat_mult functions. - * - */ - -/** - * @brief s8 convolution layer wrapper function with the main purpose to call the optimal kernel available in - cmsis-nn - * to perform the convolution. - * - * @param[in, out] ctx Function context that contains the additional buffer if required by the function. - arm_convolve_wrapper_s8_get_buffer_size will return the buffer_size if required - * @param[in] conv_params Convolution parameters (e.g. strides, dilations, pads,...). - * Range of conv_params->input_offset : [-127, 128] - * Range of conv_params->output_offset : [-128, 127] - * @param[in] quant_params Per-channel quantization info. - * It contains the multiplier and shift values to be applied to each output channel - * @param[in] input_dims Input (activation) tensor dimensions. Format: [N, H, W, C_IN] - * @param[in] input_data Input (activation) data pointer. Data type: int8 - * @param[in] filter_dims Filter tensor dimensions. Format: [C_OUT, HK, WK, C_IN] where HK and WK are the - * spatial filter dimensions - * @param[in] filter_data Filter data pointer. Data type: int8 - * @param[in] bias_dims Bias tensor dimensions. Format: [C_OUT] - * @param[in] bias_data Bias data pointer. Data type: int32 - * @param[in] output_dims Output tensor dimensions. Format: [N, H, W, C_OUT] - * @param[out] output_data Output data pointer. Data type: int8 - * - * @return The function returns either - * <code>ARM_MATH_SIZE_MISMATCH</code> if argument constraints fail. or, - * <code>ARM_MATH_SUCCESS</code> on successful completion. - * - */ -arm_status arm_convolve_wrapper_s8(const cmsis_nn_context *ctx, - const cmsis_nn_conv_params *conv_params, - const cmsis_nn_per_channel_quant_params *quant_params, - const cmsis_nn_dims *input_dims, - const q7_t *input_data, - const cmsis_nn_dims *filter_dims, - const q7_t *filter_data, - const cmsis_nn_dims *bias_dims, - const int32_t *bias_data, - const cmsis_nn_dims *output_dims, - q7_t *output_data); - -/** - * @brief Get the required buffer size for arm_convolve_wrapper_s8 - * - * @param[in] conv_params Convolution parameters (e.g. strides, dilations, pads,...). - * Range of conv_params->input_offset : [-127, 128] - * Range of conv_params->output_offset : [-128, 127] - * @param[in] input_dims Input (activation) dimensions. Format: [N, H, W, C_IN] - * @param[in] filter_dims Filter dimensions. Format: [C_OUT, HK, WK, C_IN] where HK and WK are the spatial - * filter dimensions - * @param[in] output_dims Output tensor dimensions. Format: [N, H, W, C_OUT] - * - * @return The function returns required buffer size(bytes) - * - */ -int32_t arm_convolve_wrapper_s8_get_buffer_size(const cmsis_nn_conv_params *conv_params, - const cmsis_nn_dims *input_dims, - const cmsis_nn_dims *filter_dims, - const cmsis_nn_dims *output_dims); - -/** - * @brief s16 convolution layer wrapper function with the main purpose to call the optimal kernel available in - cmsis-nn - * to perform the convolution. - * - * @param[in, out] ctx Function context that contains the additional buffer if required by the function. - arm_convolve_wrapper_s8_get_buffer_size will return the buffer_size if required - * @param[in] conv_params Convolution parameters (e.g. strides, dilations, pads,...). - * conv_params->input_offset : Not used - * conv_params->output_offset : Not used - * @param[in] quant_params Per-channel quantization info. - * It contains the multiplier and shift values to be applied to each output channel - * @param[in] input_dims Input (activation) tensor dimensions. Format: [N, H, W, C_IN] - * @param[in] input_data Input (activation) data pointer. Data type: int16 - * @param[in] filter_dims Filter tensor dimensions. Format: [C_OUT, HK, WK, C_IN] where HK and WK are the - * spatial filter dimensions - * @param[in] filter_data Filter data pointer. Data type: int8 - * @param[in] bias_dims Bias tensor dimensions. Format: [C_OUT] - * @param[in] bias_data Bias data pointer. Data type: int64 - * @param[in] output_dims Output tensor dimensions. Format: [N, H, W, C_OUT] - * @param[out] output_data Output data pointer. Data type: int16 - * - * @return The function returns either - * <code>ARM_MATH_SIZE_MISMATCH</code> if argument constraints fail. or, - * <code>ARM_MATH_SUCCESS</code> on successful completion. - * - */ -arm_status arm_convolve_wrapper_s16(const cmsis_nn_context *ctx, - const cmsis_nn_conv_params *conv_params, - const cmsis_nn_per_channel_quant_params *quant_params, - const cmsis_nn_dims *input_dims, - const q15_t *input_data, - const cmsis_nn_dims *filter_dims, - const q7_t *filter_data, - const cmsis_nn_dims *bias_dims, - const int64_t *bias_data, - const cmsis_nn_dims *output_dims, - q15_t *output_data); - -/** - * @brief Get the required buffer size for arm_convolve_wrapper_s16 - * - * @param[in] conv_params Convolution parameters (e.g. strides, dilations, pads,...). - * conv_params->input_offset : Not used - * conv_params->output_offset : Not used - * @param[in] input_dims Input (activation) dimensions. Format: [N, H, W, C_IN] - * @param[in] filter_dims Filter dimensions. Format: [C_OUT, HK, WK, C_IN] where HK and WK are the spatial - * filter dimensions - * @param[in] output_dims Output tensor dimensions. Format: [N, H, W, C_OUT] - * - * @return The function returns required buffer size(bytes) - * - */ -int32_t arm_convolve_wrapper_s16_get_buffer_size(const cmsis_nn_conv_params *conv_params, - const cmsis_nn_dims *input_dims, - const cmsis_nn_dims *filter_dims, - const cmsis_nn_dims *output_dims); - -/** - * @brief Basic s8 convolution function - * @param[in, out] ctx Function context that contains the additional buffer if required by the function. - arm_convolve_s8_get_buffer_size will return the buffer_size if required - * @param[in] conv_params Convolution parameters (e.g. strides, dilations, pads,...). - * Range of conv_params->input_offset : [-127, 128] - * Range of conv_params->output_offset : [-128, 127] - * @param[in] quant_params Per-channel quantization info. - * It contains the multiplier and shift values to be applied to each output channel - * @param[in] input_dims Input (activation) tensor dimensions. Format: [N, H, W, C_IN] - * @param[in] input_data Input (activation) data pointer. Data type: int8 - * @param[in] filter_dims Filter tensor dimensions. Format: [C_OUT, HK, WK, C_IN] where HK and WK are the - * spatial filter dimensions - * @param[in] filter_data Filter data pointer. Data type: int8 - * @param[in] bias_dims Bias tensor dimensions. Format: [C_OUT] - * @param[in] bias_data Optional bias data pointer. Data type: int32 - * @param[in] output_dims Output tensor dimensions. Format: [N, H, W, C_OUT] - * @param[out] output_data Output data pointer. Data type: int8 - - * @return The function returns <code>ARM_MATH_SUCCESS</code> - * - * @details - * 1. Supported framework: TensorFlow Lite micro - * 2. q7 is used as data type eventhough it is s8 data. It is done so to be consistent with existing APIs. - * 3. Additional memory is required for optimization. Refer to argument 'ctx' for details. - * - */ -arm_status arm_convolve_s8(const cmsis_nn_context *ctx, - const cmsis_nn_conv_params *conv_params, - const cmsis_nn_per_channel_quant_params *quant_params, - const cmsis_nn_dims *input_dims, - const q7_t *input_data, - const cmsis_nn_dims *filter_dims, - const q7_t *filter_data, - const cmsis_nn_dims *bias_dims, - const int32_t *bias_data, - const cmsis_nn_dims *output_dims, - q7_t *output_data); - -/** - * @brief Get the required buffer size for s8 convolution function - * - * @param[in] input_dims Input (activation) tensor dimensions. Format: [N, H, W, C_IN] - * @param[in] filter_dims Filter tensor dimensions. Format: [C_OUT, HK, WK, C_IN] where HK and WK - * are the spatial filter dimensions - * @return The function returns required buffer size(bytes) - * - */ -int32_t arm_convolve_s8_get_buffer_size(const cmsis_nn_dims *input_dims, const cmsis_nn_dims *filter_dims); - -/** - * @brief Basic s16 convolution function - * @param[in, out] ctx Function context that contains the additional buffer if required by the function. - arm_convolve_s16_get_buffer_size will return the buffer_size if required - * @param[in] conv_params Convolution parameters (e.g. strides, dilations, pads,...). - * conv_params->input_offset : Not used - * conv_params->output_offset : Not used - * @param[in] quant_params Per-channel quantization info. - * It contains the multiplier and shift values to be applied to each output channel - * @param[in] input_dims Input (activation) tensor dimensions. Format: [N, H, W, C_IN] - * @param[in] input_data Input (activation) data pointer. Data type: int16 - * @param[in] filter_dims Filter tensor dimensions. Format: [C_OUT, HK, WK, C_IN] where HK and WK are the - * spatial filter dimensions - * @param[in] filter_data Filter data pointer. Data type: int8 - * @param[in] bias_dims Bias tensor dimensions. Format: [C_OUT] - * @param[in] bias_data Optional bias data pointer. Data type: int64 - * @param[in] output_dims Output tensor dimensions. Format: [N, H, W, C_OUT] - * @param[out] output_data Output data pointer. Data type: int16 - - * @return The function returns <code>ARM_MATH_SUCCESS</code> - * - * @details - * 1. Supported framework: TensorFlow Lite micro - * 2. q7/q15 is used as data type eventhough it is s8/s16 data. It is done so to be consistent with existing APIs. - * 3. Additional memory is required for optimization. Refer to argument 'ctx' for details. - * - */ -arm_status arm_convolve_s16(const cmsis_nn_context *ctx, - const cmsis_nn_conv_params *conv_params, - const cmsis_nn_per_channel_quant_params *quant_params, - const cmsis_nn_dims *input_dims, - const q15_t *input_data, - const cmsis_nn_dims *filter_dims, - const q7_t *filter_data, - const cmsis_nn_dims *bias_dims, - const int64_t *bias_data, - const cmsis_nn_dims *output_dims, - q15_t *output_data); -/** - * @brief Optimized s16 convolution function - * @param[in, out] ctx Function context that contains the additional buffer if required by the function. - arm_convolve_fast_s16_get_buffer_size will return the buffer_size if required - * @param[in] conv_params Convolution parameters (e.g. strides, dilations, pads,...). - * conv_params->input_offset : Not used - * conv_params->output_offset : Not used - * @param[in] quant_params Per-channel quantization info. - * It contains the multiplier and shift values to be applied to each output channel - * @param[in] input_dims Input (activation) tensor dimensions. Format: [N, H, W, C_IN] - * @param[in] input_data Input (activation) data pointer. Data type: int16 - * @param[in] filter_dims Filter tensor dimensions. Format: [C_OUT, HK, WK, C_IN] where HK and WK are the - * spatial filter dimensions. (filter_dims->w * filter_dims->h * input_dims->c) must not - exceed 512 - * @param[in] filter_data Filter data pointer. Data type: int8 - * @param[in] bias_dims Bias tensor dimensions. Format: [C_OUT] - * @param[in] bias_data Optional bias data pointer. Data type: int64 - * @param[in] output_dims Output tensor dimensions. Format: [N, H, W, C_OUT] - * @param[out] output_data Output data pointer. Data type: int16 - - * @return The function returns <code>ARM_MATH_SUCCESS</code> - * - * @details - * 1. Supported framework: TensorFlow Lite micro - * 2. q7/q15 is used as data type eventhough it is s8/s16 data. It is done so to be consistent with existing APIs. - * 3. Additional memory is required for optimization. Refer to argument 'ctx' for details. - * 4. Implementation supports kernel volumes (filter width * filter height * input channels) < 512. - * - */ - -arm_status arm_convolve_fast_s16(const cmsis_nn_context *ctx, - const cmsis_nn_conv_params *conv_params, - const cmsis_nn_per_channel_quant_params *quant_params, - const cmsis_nn_dims *input_dims, - const q15_t *input_data, - const cmsis_nn_dims *filter_dims, - const q7_t *filter_data, - const cmsis_nn_dims *bias_dims, - const int64_t *bias_data, - const cmsis_nn_dims *output_dims, - q15_t *output_data); - -/** - * @brief Get the required buffer size for s16 convolution function - * - * @param[in] input_dims Input (activation) tensor dimensions. Format: [N, H, W, C_IN] - * @param[in] filter_dims Filter tensor dimensions. Format: [C_OUT, HK, WK, C_IN] where HK and WK - * are the spatial filter dimensions - * @return The function returns required buffer size(bytes) - * - */ -int32_t arm_convolve_s16_get_buffer_size(const cmsis_nn_dims *input_dims, const cmsis_nn_dims *filter_dims); - -/** - * @brief Get the required buffer size for fast s16 convolution function - * - * @param[in] input_dims Input (activation) tensor dimensions. Format: [N, H, W, C_IN] - * @param[in] filter_dims Filter tensor dimensions. Format: [C_OUT, HK, WK, C_IN] where HK and WK - * are the spatial filter dimensions - * @return The function returns required buffer size(bytes) - * - */ -int32_t arm_convolve_fast_s16_get_buffer_size(const cmsis_nn_dims *input_dims, const cmsis_nn_dims *filter_dims); - -/** - * @brief Basic Q7 convolution function - * @param[in] Im_in pointer to input tensor - * @param[in] dim_im_in input tensor dimension - * @param[in] ch_im_in number of input tensor channels - * @param[in] wt pointer to kernel weights - * @param[in] ch_im_out number of filters, i.e., output tensor channels - * @param[in] dim_kernel filter kernel size - * @param[in] padding padding sizes - * @param[in] stride convolution stride - * @param[in] bias pointer to bias - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in,out] Im_out pointer to output tensor - * @param[in] dim_im_out output tensor dimension - * @param[in,out] bufferA pointer to buffer space for input - * @param[in,out] bufferB pointer to buffer space for output - * @return The function returns <code>ARM_MATH_SUCCESS</code> - * - */ -arm_status arm_convolve_HWC_q7_basic(const q7_t *Im_in, - const uint16_t dim_im_in, - const uint16_t ch_im_in, - const q7_t *wt, - const uint16_t ch_im_out, - const uint16_t dim_kernel, - const uint16_t padding, - const uint16_t stride, - const q7_t *bias, - const uint16_t bias_shift, - const uint16_t out_shift, - q7_t *Im_out, - const uint16_t dim_im_out, - q15_t *bufferA, - q7_t *bufferB); - -/** - * @brief Basic Q7 convolution function (non-square shape) - * @param[in] Im_in pointer to input tensor - * @param[in] dim_im_in_x input tensor dimension x - * @param[in] dim_im_in_y input tensor dimension y - * @param[in] ch_im_in number of input tensor channels - * @param[in] wt pointer to kernel weights - * @param[in] ch_im_out number of filters, i.e., output tensor channels - * @param[in] dim_kernel_x filter kernel size x - * @param[in] dim_kernel_y filter kernel size y - * @param[in] padding_x padding size x - * @param[in] padding_y padding size y - * @param[in] stride_x convolution stride x - * @param[in] stride_y convolution stride y - * @param[in] bias pointer to bias - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in,out] Im_out pointer to output tensor - * @param[in] dim_im_out_x output tensor dimension x - * @param[in] dim_im_out_y output tensor dimension y - * @param[in,out] bufferA pointer to buffer space for input - * @param[in,out] bufferB pointer to buffer space for output - * @return The function returns <code>ARM_MATH_SUCCESS</code> - */ -arm_status arm_convolve_HWC_q7_basic_nonsquare(const q7_t *Im_in, - const uint16_t dim_im_in_x, - const uint16_t dim_im_in_y, - const uint16_t ch_im_in, - const q7_t *wt, - const uint16_t ch_im_out, - const uint16_t dim_kernel_x, - const uint16_t dim_kernel_y, - const uint16_t padding_x, - const uint16_t padding_y, - const uint16_t stride_x, - const uint16_t stride_y, - const q7_t *bias, - const uint16_t bias_shift, - const uint16_t out_shift, - q7_t *Im_out, - const uint16_t dim_im_out_x, - const uint16_t dim_im_out_y, - q15_t *bufferA, - q7_t *bufferB); - -/** - * @brief Basic Q15 convolution function - * @param[in] Im_in pointer to input tensor - * @param[in] dim_im_in input tensor dimension - * @param[in] ch_im_in number of input tensor channels - * @param[in] wt pointer to kernel weights - * @param[in] ch_im_out number of filters, i.e., output tensor channels - * @param[in] dim_kernel filter kernel size - * @param[in] padding padding sizes - * @param[in] stride convolution stride - * @param[in] bias pointer to bias - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in,out] Im_out pointer to output tensor - * @param[in] dim_im_out output tensor dimension - * @param[in,out] bufferA pointer to buffer space for input - * @param[in,out] bufferB pointer to buffer space for output - * @return The function returns <code>ARM_MATH_SUCCESS</code> - * - */ -arm_status arm_convolve_HWC_q15_basic(const q15_t *Im_in, - const uint16_t dim_im_in, - const uint16_t ch_im_in, - const q15_t *wt, - const uint16_t ch_im_out, - const uint16_t dim_kernel, - const uint16_t padding, - const uint16_t stride, - const q15_t *bias, - const uint16_t bias_shift, - const uint16_t out_shift, - q15_t *Im_out, - const uint16_t dim_im_out, - q15_t *bufferA, - q7_t *bufferB); - -/** - * @brief Fast Q7 convolution function - * @param[in] Im_in pointer to input tensor - * @param[in] dim_im_in input tensor dimension - * @param[in] ch_im_in number of input tensor channels - * @param[in] wt pointer to kernel weights - * @param[in] ch_im_out number of filters, i.e., output tensor channels - * @param[in] dim_kernel filter kernel size - * @param[in] padding padding sizes - * @param[in] stride convolution stride - * @param[in] bias pointer to bias - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in,out] Im_out pointer to output tensor - * @param[in] dim_im_out output tensor dimension - * @param[in,out] bufferA pointer to buffer space for input - * @param[in,out] bufferB pointer to buffer space for output - * @return The function returns either - * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking. - * - * This function is the version with full list of optimization tricks, but with - * some contraints: - * ch_im_in is multiple of 4 - * ch_im_out is multiple of 2 - */ -arm_status arm_convolve_HWC_q7_fast(const q7_t *Im_in, - const uint16_t dim_im_in, - const uint16_t ch_im_in, - const q7_t *wt, - const uint16_t ch_im_out, - const uint16_t dim_kernel, - const uint16_t padding, - const uint16_t stride, - const q7_t *bias, - const uint16_t bias_shift, - const uint16_t out_shift, - q7_t *Im_out, - const uint16_t dim_im_out, - q15_t *bufferA, - q7_t *bufferB); - -/** - * @brief Fast Q7 convolution function (non-sqaure shape) - * @param[in] Im_in pointer to input tensor - * @param[in] dim_im_in_x input tensor dimension x - * @param[in] dim_im_in_y input tensor dimension y - * @param[in] ch_im_in number of input tensor channels - * @param[in] wt pointer to kernel weights - * @param[in] ch_im_out number of filters, i.e., output tensor channels - * @param[in] dim_kernel_x filter kernel size x - * @param[in] dim_kernel_y filter kernel size y - * @param[in] padding_x padding size x - * @param[in] padding_y padding size y - * @param[in] stride_x convolution stride x - * @param[in] stride_y convolution stride y - * @param[in] bias pointer to bias - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in,out] Im_out pointer to output tensor - * @param[in] dim_im_out_x output tensor dimension x - * @param[in] dim_im_out_y output tensor dimension y - * @param[in,out] bufferA pointer to buffer space for input - * @param[in,out] bufferB pointer to buffer space for output - * @return The function returns either - * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking. - * - * This function is the version with full list of optimization tricks, but with - * some contraints: - * ch_im_in is multiple of 4 - * ch_im_out is multiple of 2 - */ - -arm_status arm_convolve_HWC_q7_fast_nonsquare(const q7_t *Im_in, - const uint16_t dim_im_in_x, - const uint16_t dim_im_in_y, - const uint16_t ch_im_in, - const q7_t *wt, - const uint16_t ch_im_out, - const uint16_t dim_kernel_x, - const uint16_t dim_kernel_y, - const uint16_t padding_x, - const uint16_t padding_y, - const uint16_t stride_x, - const uint16_t stride_y, - const q7_t *bias, - const uint16_t bias_shift, - const uint16_t out_shift, - q7_t *Im_out, - const uint16_t dim_im_out_x, - const uint16_t dim_im_out_y, - q15_t *bufferA, - q7_t *bufferB); - -/** - * @brief Fast Q7 version of 1x1 convolution (non-sqaure shape) - * @param[in] Im_in pointer to input tensor - * @param[in] dim_im_in_x input tensor dimension x - * @param[in] dim_im_in_y input tensor dimension y - * @param[in] ch_im_in number of input tensor channels - * @param[in] wt pointer to kernel weights - * @param[in] ch_im_out number of filters, i.e., output tensor channels - * @param[in] dim_kernel_x filter kernel size x - * @param[in] dim_kernel_y filter kernel size y - * @param[in] padding_x padding size x - * @param[in] padding_y padding size y - * @param[in] stride_x convolution stride x - * @param[in] stride_y convolution stride y - * @param[in] bias pointer to bias - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in,out] Im_out pointer to output tensor - * @param[in] dim_im_out_x output tensor dimension x - * @param[in] dim_im_out_y output tensor dimension y - * @param[in,out] bufferA pointer to buffer space for input - * @param[in,out] bufferB pointer to buffer space for output - * @return The function returns either - * <code>ARM_MATH_SIZE_MISMATCH</code> if argument constraints fail. or, - * <code>ARM_MATH_SUCCESS</code> on successful completion. - * - * This function implement convolution with 1x1 kernel size (i.e., dim_kernel_x=1 - * and dim_kernel_y=1). It can be used for - * second half of MobileNets after depthwise separable convolution. - * - * This function is the version with full list of optimization tricks, but with - * some contraints: - * ch_im_in is multiple of 4 - * ch_im_out is multiple of 2 - */ -arm_status arm_convolve_1x1_HWC_q7_fast_nonsquare(const q7_t *Im_in, - const uint16_t dim_im_in_x, - const uint16_t dim_im_in_y, - const uint16_t ch_im_in, - const q7_t *wt, - const uint16_t ch_im_out, - const uint16_t dim_kernel_x, - const uint16_t dim_kernel_y, - const uint16_t padding_x, - const uint16_t padding_y, - const uint16_t stride_x, - const uint16_t stride_y, - const q7_t *bias, - const uint16_t bias_shift, - const uint16_t out_shift, - q7_t *Im_out, - const uint16_t dim_im_out_x, - const uint16_t dim_im_out_y, - q15_t *bufferA, - q7_t *bufferB); - -/** - * @brief Fast s8 version for 1x1 convolution (non-square shape) - * - * @param[in, out] ctx Function context that contains the additional buffer if required by the function. - arm_convolve_1x1_s8_fast_get_buffer_size will return the buffer_size if required - * @param[in] conv_params Convolution parameters (e.g. strides, dilations, pads,...). - * Range of conv_params->input_offset : [-127, 128] - * Range of conv_params->output_offset : [-128, 127] - * @param[in] quant_params Per-channel quantization info. - * It contains the multiplier and shift values to be applied to each output channel - * @param[in] input_dims Input (activation) tensor dimensions. Format: [N, H, W, C_IN] - * @param[in] input_data Input (activation) data pointer. Data type: int8 - * @param[in] filter_dims Filter tensor dimensions. Format: [C_OUT, 1, 1, C_IN] - * @param[in] filter_data Filter data pointer. Data type: int8 - * @param[in] bias_dims Bias tensor dimensions. Format: [C_OUT] - * @param[in] bias_data Optional bias data pointer. Data type: int32 - * @param[in] output_dims Output tensor dimensions. Format: [N, H, W, C_OUT] - * @param[out] output_data Output data pointer. Data type: int8 - * - * @return The function returns either - * <code>ARM_MATH_SIZE_MISMATCH</code> if argument constraints fail. or, - * <code>ARM_MATH_SUCCESS</code> on successful completion. - * - * @details - * - Supported framework : TensorFlow Lite Micro - * - The following constrains on the arguments apply - * -# input_dims->c is a multiple of 4 - * -# conv_params->padding.w = conv_params->padding.h = 0 - * -# conv_params->stride.w = conv_params->stride.h = 1 - * - */ -arm_status arm_convolve_1x1_s8_fast(const cmsis_nn_context *ctx, - const cmsis_nn_conv_params *conv_params, - const cmsis_nn_per_channel_quant_params *quant_params, - const cmsis_nn_dims *input_dims, - const q7_t *input_data, - const cmsis_nn_dims *filter_dims, - const q7_t *filter_data, - const cmsis_nn_dims *bias_dims, - const int32_t *bias_data, - const cmsis_nn_dims *output_dims, - q7_t *output_data); - -/** - * @brief Get the required buffer size for arm_convolve_1x1_s8_fast - * - * @param[in] input_dims Input (activation) dimensions - * @return The function returns the required buffer size in bytes - * - */ -int32_t arm_convolve_1x1_s8_fast_get_buffer_size(const cmsis_nn_dims *input_dims); - -/** - * @brief 1xn convolution - * - * @param[in, out] ctx Function context that contains the additional buffer if required by the function. - arm_convolve_1_x_n_s8_get_buffer_size will return the buffer_size if required - * @param[in] conv_params Convolution parameters (e.g. strides, dilations, pads,...). - * Range of conv_params->input_offset : [-127, 128] - * Range of conv_params->output_offset : [-128, 127] - * @param[in] quant_params Per-channel quantization info. - * It contains the multiplier and shift values to be applied to each output channel - * @param[in] input_dims Input (activation) tensor dimensions. Format: [N, H, W, C_IN] - * @param[in] input_data Input (activation) data pointer. Data type: int8 - * @param[in] filter_dims Filter tensor dimensions. Format: [C_OUT, 1, WK, C_IN] where WK is the horizontal - * spatial filter dimension - * @param[in] filter_data Filter data pointer. Data type: int8 - * @param[in] bias_dims Bias tensor dimensions. Format: [C_OUT] - * @param[in] bias_data Optional bias data pointer. Data type: int32 - * @param[in] output_dims Output tensor dimensions. Format: [N, H, W, C_OUT] - * @param[out] output_data Output data pointer. Data type: int8 - * - * @return The function returns either - * <code>ARM_MATH_SIZE_MISMATCH</code> if argument constraints fail. or, - * <code>ARM_MATH_SUCCESS</code> on successful completion. - * - * @details - * - Supported framework : TensorFlow Lite Micro - * - The following constrains on the arguments apply - * -# input_dims->n equals 1 - * -# ouput_dims->w is a multiple of 4 - * -# Explicit constraints(since it is for 1xN convolution) - * -## input_dims->h equals 1 - * -## output_dims->h equals 1 - * -## filter_dims->h equals 1 - *@todo Remove constraint on output_dims->w to make the function generic. - * - */ -arm_status arm_convolve_1_x_n_s8(const cmsis_nn_context *ctx, - const cmsis_nn_conv_params *conv_params, - const cmsis_nn_per_channel_quant_params *quant_params, - const cmsis_nn_dims *input_dims, - const q7_t *input_data, - const cmsis_nn_dims *filter_dims, - const q7_t *filter_data, - const cmsis_nn_dims *bias_dims, - const int32_t *bias_data, - const cmsis_nn_dims *output_dims, - q7_t *output_data); - -/** - * @brief Get the required additional buffer size for 1xn convolution - * - * @param[in] input_dims Input (activation) tensor dimensions. Format: [N, H, W, C_IN] - * @param[in] filter_dims Filter tensor dimensions. Format: [C_OUT, 1, WK, C_IN] where WK is the - * horizontal spatial filter dimension - * @return The function returns required buffer size(bytes) - * - */ -int32_t arm_convolve_1_x_n_s8_get_buffer_size(const cmsis_nn_dims *input_dims, const cmsis_nn_dims *filter_dims); - -/** - * @brief Q7 version of convolution for RGB image - * @param[in] Im_in pointer to input tensor - * @param[in] dim_im_in input tensor dimension - * @param[in] ch_im_in number of input tensor channels - * @param[in] wt pointer to kernel weights - * @param[in] ch_im_out number of filters, i.e., output tensor channels - * @param[in] dim_kernel filter kernel size - * @param[in] padding padding sizes - * @param[in] stride convolution stride - * @param[in] bias pointer to bias - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in,out] Im_out pointer to output tensor - * @param[in] dim_im_out output tensor dimension - * @param[in,out] bufferA pointer to buffer space for input - * @param[in,out] bufferB pointer to buffer space for output - * @return The function returns either - * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking. - * - * This kernel is written exclusively for convolution with ch_im_in - * equals 3. This applies on the first layer of CNNs which has input - * image with RGB format. - */ - -arm_status arm_convolve_HWC_q7_RGB(const q7_t *Im_in, - const uint16_t dim_im_in, - const uint16_t ch_im_in, - const q7_t *wt, - const uint16_t ch_im_out, - const uint16_t dim_kernel, - const uint16_t padding, - const uint16_t stride, - const q7_t *bias, - const uint16_t bias_shift, - const uint16_t out_shift, - q7_t *Im_out, - const uint16_t dim_im_out, - q15_t *bufferA, - q7_t *bufferB); - -/** - * @brief Fast Q15 convolution function - * @param[in] Im_in pointer to input tensor - * @param[in] dim_im_in input tensor dimension - * @param[in] ch_im_in number of input tensor channels - * @param[in] wt pointer to kernel weights - * @param[in] ch_im_out number of filters, i.e., output tensor channels - * @param[in] dim_kernel filter kernel size - * @param[in] padding padding sizes - * @param[in] stride convolution stride - * @param[in] bias pointer to bias - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in,out] Im_out pointer to output tensor - * @param[in] dim_im_out output tensor dimension - * @param[in,out] bufferA pointer to buffer space for input - * @param[in,out] bufferB pointer to buffer space for output - * @return The function returns either - * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking. - * - * This function is the version with full list of optimization tricks, but with - * some contraints: - * ch_im_in is multiple of 2 - * ch_im_out is multiple of 2 - * dim_im_out is a multiple of 2 - */ - -arm_status arm_convolve_HWC_q15_fast(const q15_t *Im_in, - const uint16_t dim_im_in, - const uint16_t ch_im_in, - const q15_t *wt, - const uint16_t ch_im_out, - const uint16_t dim_kernel, - const uint16_t padding, - const uint16_t stride, - const q15_t *bias, - const uint16_t bias_shift, - const uint16_t out_shift, - q15_t *Im_out, - const uint16_t dim_im_out, - q15_t *bufferA, - q7_t *bufferB); - -/** - * @brief Fast Q15 convolution function (non-sqaure shape) - * @param[in] Im_in pointer to input tensor - * @param[in] dim_im_in_x input tensor dimension x - * @param[in] dim_im_in_y input tensor dimension y - * @param[in] ch_im_in number of input tensor channels - * @param[in] wt pointer to kernel weights - * @param[in] ch_im_out number of filters, i.e., output tensor channels - * @param[in] dim_kernel_x filter kernel size x - * @param[in] dim_kernel_y filter kernel size y - * @param[in] padding_x padding size x - * @param[in] padding_y padding size y - * @param[in] stride_x convolution stride x - * @param[in] stride_y convolution stride y - * @param[in] bias pointer to bias - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in,out] Im_out pointer to output tensor - * @param[in] dim_im_out_x output tensor dimension x - * @param[in] dim_im_out_y output tensor dimension y - * @param[in,out] bufferA pointer to buffer space for input - * @param[in,out] bufferB pointer to buffer space for output - * @return The function returns either - * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking. - * - * @details - * - * <b>Buffer size:</b> - * - * bufferA size: 2*ch_im_in*dim_kernel*dim_kernel - * - * bufferB size: 0 - * - * <b>Input dimension constraints:</b> - * - * ch_im_in is multiple of 2 - * - * ch_im_out is multipe of 2 - * - */ - -arm_status arm_convolve_HWC_q15_fast_nonsquare(const q15_t *Im_in, - const uint16_t dim_im_in_x, - const uint16_t dim_im_in_y, - const uint16_t ch_im_in, - const q15_t *wt, - const uint16_t ch_im_out, - const uint16_t dim_kernel_x, - const uint16_t dim_kernel_y, - const uint16_t padding_x, - const uint16_t padding_y, - const uint16_t stride_x, - const uint16_t stride_y, - const q15_t *bias, - const uint16_t bias_shift, - const uint16_t out_shift, - q15_t *Im_out, - const uint16_t dim_im_out_x, - const uint16_t dim_im_out_y, - q15_t *bufferA, - q7_t *bufferB); - -/** - * @brief Q7 depthwise separable convolution function - * @param[in] Im_in pointer to input tensor - * @param[in] dim_im_in input tensor dimension - * @param[in] ch_im_in number of input tensor channels - * @param[in] wt pointer to kernel weights - * @param[in] ch_im_out number of filters, i.e., output tensor channels - * @param[in] dim_kernel filter kernel size - * @param[in] padding padding sizes - * @param[in] stride convolution stride - * @param[in] bias pointer to bias - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in,out] Im_out pointer to output tensor - * @param[in] dim_im_out output tensor dimension - * @param[in,out] bufferA pointer to buffer space for input - * @param[in,out] bufferB pointer to buffer space for output - * @return The function returns either - * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking. - * - * This function is the version with full list of optimization tricks, but with - * some contraints: - * ch_im_in is multiple of 2 - * ch_im_out is multiple of 2 - */ - -arm_status arm_depthwise_separable_conv_HWC_q7(const q7_t *Im_in, - const uint16_t dim_im_in, - const uint16_t ch_im_in, - const q7_t *wt, - const uint16_t ch_im_out, - const uint16_t dim_kernel, - const uint16_t padding, - const uint16_t stride, - const q7_t *bias, - const uint16_t bias_shift, - const uint16_t out_shift, - q7_t *Im_out, - const uint16_t dim_im_out, - q15_t *bufferA, - q7_t *bufferB); - -/** - * @brief Q7 depthwise separable convolution function (non-square shape) - * @param[in] Im_in pointer to input tensor - * @param[in] dim_im_in_x input tensor dimension x - * @param[in] dim_im_in_y input tensor dimension y - * @param[in] ch_im_in number of input tensor channels - * @param[in] wt pointer to kernel weights - * @param[in] ch_im_out number of filters, i.e., output tensor channels - * @param[in] dim_kernel_x filter kernel size x - * @param[in] dim_kernel_y filter kernel size y - * @param[in] padding_x padding sizes x - * @param[in] padding_y padding sizes y - * @param[in] stride_x convolution stride x - * @param[in] stride_y convolution stride y - * @param[in] bias pointer to bias - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in,out] Im_out pointer to output tensor - * @param[in] dim_im_out_x output tensor dimension x - * @param[in] dim_im_out_y output tensor dimension y - * @param[in,out] bufferA pointer to buffer space for input - * @param[in,out] bufferB pointer to buffer space for output - * @return The function returns either - * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking. - * - * This function is the version with full list of optimization tricks, but with - * some contraints: - * ch_im_in is multiple of 2 - * ch_im_out is multiple of 2 - */ -arm_status arm_depthwise_separable_conv_HWC_q7_nonsquare(const q7_t *Im_in, - const uint16_t dim_im_in_x, - const uint16_t dim_im_in_y, - const uint16_t ch_im_in, - const q7_t *wt, - const uint16_t ch_im_out, - const uint16_t dim_kernel_x, - const uint16_t dim_kernel_y, - const uint16_t padding_x, - const uint16_t padding_y, - const uint16_t stride_x, - const uint16_t stride_y, - const q7_t *bias, - const uint16_t bias_shift, - const uint16_t out_shift, - q7_t *Im_out, - const uint16_t dim_im_out_x, - const uint16_t dim_im_out_y, - q15_t *bufferA, - q7_t *bufferB); - -/** - * @brief Wrapper function to pick the right optimized s8 depthwise convolution function - * - * @param[in, out] ctx Function context (e.g. temporary buffer). Check the function - * definition file to see if an additional buffer is required. - * Optional function {API}_get_buffer_size() provides the buffer - * size if required. - * @param[in] dw_conv_params Depthwise convolution parameters (e.g. strides, dilations, pads,...) - * dw_conv_params->dilation is not used. - * Range of dw_conv_params->input_offset : [-127, 128] - * Range of dw_conv_params->output_offset : [-128, 127] - * @param[in] quant_params Per-channel quantization info. - * It contains the multiplier and shift values to be applied to each - * output channel - * @param[in] input_dims Input (activation) tensor dimensions. Format: [H, W, C_IN] - * Batch argument N is not used and assumed to be 1. - * @param[in] input_data Input (activation) data pointer. Data type: int8 - * @param[in] filter_dims Filter tensor dimensions. Format: [1, H, W, C_OUT] - * @param[in] filter_data Filter data pointer. Data type: int8 - * @param[in] bias_dims Bias tensor dimensions. Format: [C_OUT] - * @param[in] bias_data Bias data pointer. Data type: int32 - * @param[in] output_dims Output tensor dimensions. Format: [1, H, W, C_OUT] - * @param[in, out] output_data Output data pointer. Data type: int8 - * @return The function returns - * <code>ARM_MATH_SUCCESS</code> - Successful completion. - * - * @details - * - Supported framework: TensorFlow Lite - * - Picks one of the the following functions - * -# arm_depthwise_conv_s8() - * -# arm_depthwise_conv_3x3_s8() - Cortex-M CPUs with DSP extension only - * -# arm_depthwise_conv_s8_opt() - * - q7 is used as data type eventhough it is s8 data. It is done so to be consistent with existing APIs. - * - Check details of arm_depthwise_conv_s8_opt() for potential data that can be accessed outside of the - * boundary. - */ -arm_status arm_depthwise_conv_wrapper_s8(const cmsis_nn_context *ctx, - const cmsis_nn_dw_conv_params *dw_conv_params, - const cmsis_nn_per_channel_quant_params *quant_params, - const cmsis_nn_dims *input_dims, - const q7_t *input_data, - const cmsis_nn_dims *filter_dims, - const q7_t *filter_data, - const cmsis_nn_dims *bias_dims, - const int32_t *bias_data, - const cmsis_nn_dims *output_dims, - q7_t *output_data); - -/** - * @brief Get size of additional buffer required by arm_depthwise_conv_wrapper_s8() - * - * @param[in] dw_conv_params Depthwise convolution parameters (e.g. strides, dilations, pads,...) - * dw_conv_params->dilation is not used. - * Range of dw_conv_params->input_offset : [-127, 128] - * Range of dw_conv_params->input_offset : [-128, 127] - * @param[in] input_dims Input (activation) tensor dimensions. Format: [H, W, C_IN] - * Batch argument N is not used and assumed to be 1. - * @param[in] filter_dims Filter tensor dimensions. Format: [1, H, W, C_OUT] - * @param[in] output_dims Output tensor dimensions. Format: [1, H, W, C_OUT] - * @return Size of additional memory required for optimizations in bytes. - * - */ -int32_t arm_depthwise_conv_wrapper_s8_get_buffer_size(const cmsis_nn_dw_conv_params *dw_conv_params, - const cmsis_nn_dims *input_dims, - const cmsis_nn_dims *filter_dims, - const cmsis_nn_dims *output_dims); - -/** - * @brief Basic s8 depthwise convolution function that doesn't have any constraints on the input dimensions. - * - * @param[in, out] ctx Function context (e.g. temporary buffer). Check the function - * definition file to see if an additional buffer is required. - * Optional function {API}_get_buffer_size() provides the buffer - * size if an additional buffer is required. - * exists if additional memory is. - * @param[in] dw_conv_params Depthwise convolution parameters (e.g. strides, dilations, pads,...) - * dw_conv_params->dilation is not used. - * Range of dw_conv_params->input_offset : [-127, 128] - * Range of dw_conv_params->input_offset : [-128, 127] - * @param[in] quant_params Per-channel quantization info. - * It contains the multiplier and shift values to be applied to each - * output channel - * @param[in] input_dims Input (activation) tensor dimensions. Format: [N, H, W, C_IN] - * Batch argument N is not used. - * @param[in] input_data Input (activation) data pointer. Data type: int8 - * @param[in] filter_dims Filter tensor dimensions. Format: [1, H, W, C_OUT] - * @param[in] filter_data Filter data pointer. Data type: int8 - * @param[in] bias_dims Bias tensor dimensions. Format: [C_OUT] - * @param[in] bias_data Bias data pointer. Data type: int32 - * @param[in] output_dims Output tensor dimensions. Format: [N, H, W, C_OUT] - * @param[in, out] output_data Output data pointer. Data type: int8 - * @return The function returns <code>ARM_MATH_SUCCESS</code> - * - * @details - * - Supported framework: TensorFlow Lite - * - q7 is used as data type eventhough it is s8 data. It is done so to be consistent with existing APIs. - */ -arm_status arm_depthwise_conv_s8(const cmsis_nn_context *ctx, - const cmsis_nn_dw_conv_params *dw_conv_params, - const cmsis_nn_per_channel_quant_params *quant_params, - const cmsis_nn_dims *input_dims, - const q7_t *input_data, - const cmsis_nn_dims *filter_dims, - const q7_t *filter_data, - const cmsis_nn_dims *bias_dims, - const int32_t *bias_data, - const cmsis_nn_dims *output_dims, - q7_t *output_data); - -/** - * @brief Basic s16 depthwise convolution function that doesn't have any constraints on the input dimensions. - * - * @param[in, out] ctx Function context (e.g. temporary buffer). Check the function - * definition file to see if an additional buffer is required. - * Optional function {API}_get_buffer_size() provides the buffer - * size if an additional buffer is required. - * exists if additional memory is. - * @param[in] dw_conv_params Depthwise convolution parameters (e.g. strides, dilations, pads,...) - * conv_params->input_offset : Not used - * conv_params->output_offset : Not used - * @param[in] quant_params Per-channel quantization info. - * It contains the multiplier and shift values to be applied to each - * output channel - * @param[in] input_dims Input (activation) tensor dimensions. Format: [N, H, W, C_IN] - * Batch argument N is not used. - * @param[in] input_data Input (activation) data pointer. Data type: int8 - * @param[in] filter_dims Filter tensor dimensions. Format: [1, H, W, C_OUT] - * @param[in] filter_data Filter data pointer. Data type: int8 - * @param[in] bias_dims Bias tensor dimensions. Format: [C_OUT] - * @param[in] bias_data Bias data pointer. Data type: int64 - * @param[in] output_dims Output tensor dimensions. Format: [N, H, W, C_OUT] - * @param[in, out] output_data Output data pointer. Data type: int16 - * @return The function returns <code>ARM_MATH_SUCCESS</code> - * - * @details - * - Supported framework: TensorFlow Lite - * - q15 is used as data type eventhough it is s16 data. It is done so to be consistent with existing APIs. - */ -arm_status arm_depthwise_conv_s16(const cmsis_nn_context *ctx, - const cmsis_nn_dw_conv_params *dw_conv_params, - const cmsis_nn_per_channel_quant_params *quant_params, - const cmsis_nn_dims *input_dims, - const q15_t *input_data, - const cmsis_nn_dims *filter_dims, - const q7_t *filter_data, - const cmsis_nn_dims *bias_dims, - const int64_t *bias_data, - const cmsis_nn_dims *output_dims, - q15_t *output_data); - -/** - * @brief Optimized s8 depthwise convolution function for 3x3 kernel size with some constraints on - * the input arguments(documented below). Refer arm_depthwise_conv_s8() for function - * argument details. - * - * @return The function returns one of the following - * <code>ARM_MATH_SIZE_MISMATCH</code> - Unsupported dimension of tensors - * <code>ARM_MATH_ARGUMENT_ERROR</code> - Unsupported pad size along the x axis - * <code>ARM_MATH_SUCCESS</code> - Successful operation - * - * @details - * - Supported framework : TensorFlow Lite Micro - * - The following constrains on the arguments apply - * -# Number of input channel equals number of output channels - * -# Filter height and width equals 3 - * -# Padding along x is either 0 or 1. - * - */ -arm_status arm_depthwise_conv_3x3_s8(const cmsis_nn_context *ctx, - const cmsis_nn_dw_conv_params *dw_conv_params, - const cmsis_nn_per_channel_quant_params *quant_params, - const cmsis_nn_dims *input_dims, - const q7_t *input_data, - const cmsis_nn_dims *filter_dims, - const q7_t *filter_data, - const cmsis_nn_dims *bias_dims, - const int32_t *bias_data, - const cmsis_nn_dims *output_dims, - q7_t *output_data); - -/** - * @brief Optimized s8 depthwise convolution function with constraint that in_channel equals out_channel. - * Refer arm_depthwise_conv_s8() for function argument details. - * - * @return The function returns one of the following - * <code>ARM_MATH_SIZE_MISMATCH</code> - input channel != output channel or - * ch_mult != 1 - * <code>ARM_MATH_SUCCESS</code> - Successful operation - * - * @note If number of channels is not a multiple of 4, upto 3 elements outside the boundary will be read out - * for the following if MVE optimizations(Arm Helium Technology) are used. - * - Output shift - * - Output multiplier - * - Output bias - * - kernel - * @details - * - Supported framework: TensorFlow Lite - * - The following constrains on the arguments apply - * -# Number of input channel equals number of output channels or ch_mult equals 1 - * - q7 is used as data type eventhough it is s8 data. It is done so to be consistent with existing APIs. - * - Reccomended when number of channels is 4 or greater. - * - */ -arm_status arm_depthwise_conv_s8_opt(const cmsis_nn_context *ctx, - const cmsis_nn_dw_conv_params *dw_conv_params, - const cmsis_nn_per_channel_quant_params *quant_params, - const cmsis_nn_dims *input_dims, - const q7_t *input_data, - const cmsis_nn_dims *filter_dims, - const q7_t *filter_data, - const cmsis_nn_dims *bias_dims, - const int32_t *bias_data, - const cmsis_nn_dims *output_dims, - q7_t *output_data); - -/** - * @brief Get the required buffer size for optimized s8 depthwise convolution - * function with constraint that in_channel equals out_channel. - * @param[in] input_dims Input (activation) tensor dimensions. Format: [1, H, W, C_IN] - * Batch argument N is not used. - * @param[in] filter_dims Filter tensor dimensions. Format: [1, H, W, C_OUT] - * @return The function returns required buffer size in bytes - * - */ -int32_t arm_depthwise_conv_s8_opt_get_buffer_size(const cmsis_nn_dims *input_dims, const cmsis_nn_dims *filter_dims); - -/** - * @defgroup FC Fully-connected Layer Functions - * - * Collection of fully-connected and matrix multiplication functions. - * - * Fully-connected layer is basically a matrix-vector multiplication - * with bias. The matrix is the weights and the input/output vectors - * are the activation values. Supported {weight, activation} precisions - * include {8-bit, 8-bit}, {16-bit, 16-bit}, and {8-bit, 16-bit}. - * - * Here we have two types of kernel functions. The basic function - * implements the function using regular GEMV approach. The opt functions - * operates with weights in interleaved formats. - * - */ - -/** - *@brief Q7 basic fully-connected layer function - *@param[in] pV pointer to input vector - *@param[in] pM pointer to matrix weights - *@param[in] dim_vec length of the vector - *@param[in] num_of_rows number of rows in weight matrix - *@param[in] bias_shift amount of left-shift for bias - *@param[in] out_shift amount of right-shift for output - *@param[in] bias pointer to bias - *@param[in,out] pOut pointer to output vector - *@param[in,out] vec_buffer pointer to buffer space for input - *@return The function returns <code>ARM_MATH_SUCCESS</code> - * - */ - -arm_status arm_fully_connected_q7(const q7_t *pV, - const q7_t *pM, - const uint16_t dim_vec, - const uint16_t num_of_rows, - const uint16_t bias_shift, - const uint16_t out_shift, - const q7_t *bias, - q7_t *pOut, - q15_t *vec_buffer); - -/** - * @brief Basic s8 Fully Connected function. - * - * @param[in, out] ctx Function context (e.g. temporary buffer). Check the function - * definition file to see if an additional buffer is required. - * Optional function {API}_get_buffer_size() provides the buffer - * size if an additional buffer is required. - * @param[in] fc_params Fully Connected layer parameters. - * Range of fc_params->input_offset : [-127, 128] - * fc_params->filter_offset : 0 - * Range of fc_params->output_offset : [-128, 127] - * @param[in] quant_params Per-tensor quantization info. - * It contains the multiplier and shift values to be applied to the output tensor. - * @param[in] input_dims Input (activation) tensor dimensions. Format: [N, H, W, C_IN] - * Input dimension is taken as Nx(H * W * C_IN) - * @param[in] input_data Input (activation) data pointer. Data type: int8 - * @param[in] filter_dims Two dimensional filter dimensions. Format: [N, C] - * N : accumulation depth and equals (H * W * C_IN) from input_dims - * C : output depth and equals C_OUT in output_dims - * H & W : Not used - * @param[in] filter_data Filter data pointer. Data type: int8 - * @param[in] bias_dims Bias tensor dimensions. Format: [C_OUT] - * N, H, W : Not used - * @param[in] bias_data Bias data pointer. Data type: int32 - * @param[in] output_dims Output tensor dimensions. Format: [N, C_OUT] - * N : Batches - * C_OUT : Output depth - * H & W : Not used. - * @param[in, out] output_data Output data pointer. Data type: int8 - * @return The function returns <code>ARM_MATH_SUCCESS</code> - * - * @details - * - Supported framework: TensorFlow Lite - * - q7 is used as data type eventhough it is s8 data. It is done so to be consistent with existing APIs. - */ -arm_status arm_fully_connected_s8(const cmsis_nn_context *ctx, - const cmsis_nn_fc_params *fc_params, - const cmsis_nn_per_tensor_quant_params *quant_params, - const cmsis_nn_dims *input_dims, - const q7_t *input_data, - const cmsis_nn_dims *filter_dims, - const q7_t *filter_data, - const cmsis_nn_dims *bias_dims, - const int32_t *bias_data, - const cmsis_nn_dims *output_dims, - q7_t *output_data); - -/** - * @brief Get the required buffer size for S8 basic fully-connected and - * matrix multiplication layer function for TF Lite - * @param[in] filter_dims dimension of filter - * @return The function returns required buffer size in bytes - * - */ -int32_t arm_fully_connected_s8_get_buffer_size(const cmsis_nn_dims *filter_dims); - -/** - * @brief Basic s16 Fully Connected function. - * - * @param[in, out] ctx Function context (e.g. temporary buffer). Check the function - * definition file to see if an additional buffer is required. - * Optional function {API}_get_buffer_size() provides the buffer - * size if an additional buffer is required. - * @param[in] fc_params Fully Connected layer parameters. - * fc_params->input_offset : 0 - * fc_params->filter_offset : 0 - * fc_params->output_offset : 0 - * @param[in] quant_params Per-tensor quantization info. - * It contains the multiplier and shift values to be applied to the output tensor. - * @param[in] input_dims Input (activation) tensor dimensions. Format: [N, H, W, C_IN] - * Input dimension is taken as Nx(H * W * C_IN) - * @param[in] input_data Input (activation) data pointer. Data type: int16 - * @param[in] filter_dims Two dimensional filter dimensions. Format: [N, C] - * N : accumulation depth and equals (H * W * C_IN) from input_dims - * C : output depth and equals C_OUT in output_dims - * H & W : Not used - * @param[in] filter_data Filter data pointer. Data type: int8 - * @param[in] bias_dims Bias tensor dimensions. Format: [C_OUT] - * N, H, W : Not used - * @param[in] bias_data Bias data pointer. Data type: int64 - * @param[in] output_dims Output tensor dimensions. Format: [N, C_OUT] - * N : Batches - * C_OUT : Output depth - * H & W : Not used. - * @param[in, out] output_data Output data pointer. Data type: int16 - * @return The function returns <code>ARM_MATH_SUCCESS</code> - * - * @details - * - Supported framework: TensorFlow Lite - * - q15 is used as data type eventhough it is s16 data. It is done so to be consistent with existing APIs. - */ -arm_status arm_fully_connected_s16(const cmsis_nn_context *ctx, - const cmsis_nn_fc_params *fc_params, - const cmsis_nn_per_tensor_quant_params *quant_params, - const cmsis_nn_dims *input_dims, - const q15_t *input_data, - const cmsis_nn_dims *filter_dims, - const q7_t *filter_data, - const cmsis_nn_dims *bias_dims, - const int64_t *bias_data, - const cmsis_nn_dims *output_dims, - q15_t *output_data); - -/** - * @brief Get the required buffer size for S16 basic fully-connected and - * matrix multiplication layer function for TF Lite - * @param[in] filter_dims dimension of filter - * @return The function returns required buffer size in bytes - * - */ -int32_t arm_fully_connected_s16_get_buffer_size(const cmsis_nn_dims *filter_dims); - -/** - * @brief Q7 opt fully-connected layer function - * @param[in] pV pointer to input vector - * @param[in] pM pointer to matrix weights - * @param[in] dim_vec length of the vector - * @param[in] num_of_rows number of rows in weight matrix - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in] bias pointer to bias - * @param[in,out] pOut pointer to output vector - * @param[in,out] vec_buffer pointer to buffer space for input - * @return The function returns <code>ARM_MATH_SUCCESS</code> - * - */ - -arm_status arm_fully_connected_q7_opt(const q7_t *pV, - const q7_t *pM, - const uint16_t dim_vec, - const uint16_t num_of_rows, - const uint16_t bias_shift, - const uint16_t out_shift, - const q7_t *bias, - q7_t *pOut, - q15_t *vec_buffer); - -/** - * @brief Q15 basic fully-connected layer function - * @param[in] pV pointer to input vector - * @param[in] pM pointer to matrix weights - * @param[in] dim_vec length of the vector - * @param[in] num_of_rows number of rows in weight matrix - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in] bias pointer to bias - * @param[in,out] pOut pointer to output vector - * @param[in,out] vec_buffer pointer to buffer space for input - * @return The function returns <code>ARM_MATH_SUCCESS</code> - * - */ - -arm_status arm_fully_connected_q15(const q15_t *pV, - const q15_t *pM, - const uint16_t dim_vec, - const uint16_t num_of_rows, - const uint16_t bias_shift, - const uint16_t out_shift, - const q15_t *bias, - q15_t *pOut, - q15_t *vec_buffer); - -/** - * @brief Q15 opt fully-connected layer function - * @param[in] pV pointer to input vector - * @param[in] pM pointer to matrix weights - * @param[in] dim_vec length of the vector - * @param[in] num_of_rows number of rows in weight matrix - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in] bias pointer to bias - * @param[in,out] pOut pointer to output vector - * @param[in,out] vec_buffer pointer to buffer space for input - * @return The function returns <code>ARM_MATH_SUCCESS</code> - * - */ - -arm_status arm_fully_connected_q15_opt(const q15_t *pV, - const q15_t *pM, - const uint16_t dim_vec, - const uint16_t num_of_rows, - const uint16_t bias_shift, - const uint16_t out_shift, - const q15_t *bias, - q15_t *pOut, - q15_t *vec_buffer); - -/** - * @brief Mixed Q15-Q7 fully-connected layer function - * @param[in] pV pointer to input vector - * @param[in] pM pointer to matrix weights - * @param[in] dim_vec length of the vector - * @param[in] num_of_rows number of rows in weight matrix - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in] bias pointer to bias - * @param[in,out] pOut pointer to output vector - * @param[in,out] vec_buffer pointer to buffer space for input - * @return The function returns <code>ARM_MATH_SUCCESS</code> - * - */ - -arm_status arm_fully_connected_mat_q7_vec_q15(const q15_t *pV, - const q7_t *pM, - const uint16_t dim_vec, - const uint16_t num_of_rows, - const uint16_t bias_shift, - const uint16_t out_shift, - const q7_t *bias, - q15_t *pOut, - q15_t *vec_buffer); - -/** - * @brief Mixed Q15-Q7 opt fully-connected layer function - * @param[in] pV pointer to input vector - * @param[in] pM pointer to matrix weights - * @param[in] dim_vec length of the vector - * @param[in] num_of_rows number of rows in weight matrix - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in] bias pointer to bias - * @param[in,out] pOut pointer to output vector - * @param[in,out] vec_buffer pointer to buffer space for input - * @return The function returns <code>ARM_MATH_SUCCESS</code> - * - */ - -arm_status arm_fully_connected_mat_q7_vec_q15_opt(const q15_t *pV, - const q7_t *pM, - const uint16_t dim_vec, - const uint16_t num_of_rows, - const uint16_t bias_shift, - const uint16_t out_shift, - const q7_t *bias, - q15_t *pOut, - q15_t *vec_buffer); - -/** - * @brief Matrix-Multiplication Kernels for Convolution - * - * These functions are used within convolution layer functions for - * matrix multiplication. - * - * The implementation is similar to CMSIS-DSP arm_mat_mult functions - * with one Q7 and one Q15 operands. The Q15 operand is the im2col - * output which is always with 2 columns. - * - */ - -/** - * @brief Matrix-multiplication function for convolution - * @param[in] pA pointer to operand A - * @param[in] pInBuffer pointer to operand B, always conssists of 2 vectors - * @param[in] ch_im_out numRow of A - * @param[in] numCol_A numCol of A - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in] bias the bias - * @param[in,out] pOut pointer to output - * @return The function returns the incremented output pointer - */ - -q7_t *arm_nn_mat_mult_kernel_q7_q15(const q7_t *pA, - const q15_t *pInBuffer, - const uint16_t ch_im_out, - const uint16_t numCol_A, - const uint16_t bias_shift, - const uint16_t out_shift, - const q7_t *bias, - q7_t *pOut); - -#ifdef __cplusplus -} -#endif - -/* - * Other functions - * These layers are typically not timing critical - * Basic implementation is supported here - */ - -#ifdef __cplusplus -extern "C" { -#endif - -/** - * @defgroup BasicMath Basic math functions - * - * Elementwise add and multiplication functions. - * - */ - -/** - * @brief s8 elementwise add of two vectors - * @param[in] input_1_vect pointer to input vector 1 - * @param[in] input_2_vect pointer to input vector 2 - * @param[in] input_1_offset offset for input 1. Range: -127 to 128 - * @param[in] input_1_mult multiplier for input 1 - * @param[in] input_1_shift shift for input 1 - * @param[in] input_2_offset offset for input 2. Range: -127 to 128 - * @param[in] input_2_mult multiplier for input 2 - * @param[in] input_2_shift shift for input 2 - * @param[in] left_shift input left shift - * @param[in,out] output pointer to output vector - * @param[in] out_offset output offset. Range: -128 to 127 - * @param[in] out_mult output multiplier - * @param[in] out_shift output shift - * @param[in] out_activation_min minimum value to clamp output to. Min: -128 - * @param[in] out_activation_max maximum value to clamp output to. Max: 127 - * @param[in] block_size number of samples - * @return The function returns ARM_MATH_SUCCESS - */ -arm_status arm_elementwise_add_s8(const int8_t *input_1_vect, - const int8_t *input_2_vect, - const int32_t input_1_offset, - const int32_t input_1_mult, - const int32_t input_1_shift, - const int32_t input_2_offset, - const int32_t input_2_mult, - const int32_t input_2_shift, - const int32_t left_shift, - int8_t *output, - const int32_t out_offset, - const int32_t out_mult, - const int32_t out_shift, - const int32_t out_activation_min, - const int32_t out_activation_max, - const int32_t block_size); - -/** - * @brief s16 elementwise add of two vectors - * @param[in] input_1_vect pointer to input vector 1 - * @param[in] input_2_vect pointer to input vector 2 - * @param[in] input_1_offset offset for input 1. Not used. - * @param[in] input_1_mult multiplier for input 1 - * @param[in] input_1_shift shift for input 1 - * @param[in] input_2_offset offset for input 2. Not used. - * @param[in] input_2_mult multiplier for input 2 - * @param[in] input_2_shift shift for input 2 - * @param[in] left_shift input left shift - * @param[in,out] output pointer to output vector - * @param[in] out_offset output offset. Not used. - * @param[in] out_mult output multiplier - * @param[in] out_shift output shift - * @param[in] out_activation_min minimum value to clamp output to. Min: -32768 - * @param[in] out_activation_max maximum value to clamp output to. Max: 32767 - * @param[in] block_size number of samples - * @return The function returns ARM_MATH_SUCCESS - */ -arm_status arm_elementwise_add_s16(const int16_t *input_1_vect, - const int16_t *input_2_vect, - const int32_t input_1_offset, - const int32_t input_1_mult, - const int32_t input_1_shift, - const int32_t input_2_offset, - const int32_t input_2_mult, - const int32_t input_2_shift, - const int32_t left_shift, - int16_t *output, - const int32_t out_offset, - const int32_t out_mult, - const int32_t out_shift, - const int32_t out_activation_min, - const int32_t out_activation_max, - const int32_t block_size); - -/** - * @brief s8 elementwise multiplication - * @param[in] input_1_vect pointer to input vector 1 - * @param[in] input_2_vect pointer to input vector 2 - * @param[in] input_1_offset offset for input 1. Range: -127 to 128 - * @param[in] input_2_offset offset for input 2. Range: -127 to 128 - * @param[in,out] output pointer to output vector - * @param[in] out_offset output offset. Range: -128 to 127 - * @param[in] out_mult output multiplier - * @param[in] out_shift output shift - * @param[in] out_activation_min minimum value to clamp output to. Min: -128 - * @param[in] out_activation_max maximum value to clamp output to. Max: 127 - * @param[in] block_size number of samples - * @return The function returns ARM_MATH_SUCCESS - * - * @details Supported framework: TensorFlow Lite micro - */ -arm_status arm_elementwise_mul_s8(const int8_t *input_1_vect, - const int8_t *input_2_vect, - const int32_t input_1_offset, - const int32_t input_2_offset, - int8_t *output, - const int32_t out_offset, - const int32_t out_mult, - const int32_t out_shift, - const int32_t out_activation_min, - const int32_t out_activation_max, - const int32_t block_size); - -/** - * @brief s16 elementwise multiplication - * @param[in] input_1_vect pointer to input vector 1 - * @param[in] input_2_vect pointer to input vector 2 - * @param[in] input_1_offset offset for input 1. Not used. - * @param[in] input_2_offset offset for input 2. Not used. - * @param[in,out] output pointer to output vector - * @param[in] out_offset output offset. Not used. - * @param[in] out_mult output multiplier - * @param[in] out_shift output shift - * @param[in] out_activation_min minimum value to clamp output to. Min: -32768 - * @param[in] out_activation_max maximum value to clamp output to. Max: 32767 - * @param[in] block_size number of samples - * @return The function returns ARM_MATH_SUCCESS - * - * @details Supported framework: TensorFlow Lite micro - */ -arm_status arm_elementwise_mul_s16(const int16_t *input_1_vect, - const int16_t *input_2_vect, - const int32_t input_1_offset, - const int32_t input_2_offset, - int16_t *output, - const int32_t out_offset, - const int32_t out_mult, - const int32_t out_shift, - const int32_t out_activation_min, - const int32_t out_activation_max, - const int32_t block_size); - -/** - * @defgroup Acti Activation Functions - * - * Perform activation layers, including ReLU (Rectified Linear Unit), - * sigmoid and tanh - * - */ - -/** - * @brief Q7 RELU function - * @param[in,out] data pointer to input - * @param[in] size number of elements - * @return none. - */ - -void arm_relu_q7(q7_t *data, uint16_t size); - -/** - * @brief s8 ReLU6 function - * @param[in,out] data pointer to input - * @param[in] size number of elements - */ - -void arm_relu6_s8(q7_t *data, uint16_t size); - -/** - * @brief Q15 RELU function - * @param[in,out] data pointer to input - * @param[in] size number of elements - * @return none. - */ - -void arm_relu_q15(q15_t *data, uint16_t size); - -/** - * @brief Q7 neural network activation function using direct table look-up - * @param[in,out] data pointer to input - * @param[in] size number of elements - * @param[in] int_width bit-width of the integer part, assume to be smaller than 3 - * @param[in] type type of activation functions - * @return none. - */ - -void arm_nn_activations_direct_q7(q7_t *data, uint16_t size, uint16_t int_width, arm_nn_activation_type type); - -/** - * @brief Q15 neural network activation function using direct table look-up - * @param[in,out] data pointer to input - * @param[in] size number of elements - * @param[in] int_width bit-width of the integer part, assume to be smaller than 3 - * @param[in] type type of activation functions - * @return none. - * - * @details - * - * This is the direct table look-up approach. - * - * Assume here the integer part of the fixed-point is <= 3. - * More than 3 just not making much sense, makes no difference with - * saturation followed by any of these activation functions. - */ - -void arm_nn_activations_direct_q15(q15_t *data, uint16_t size, uint16_t int_width, arm_nn_activation_type type); - -/** - * @defgroup Pooling Pooling Functions - * - * Perform pooling functions, including max pooling and average pooling - * - */ - -/** - * @brief Q7 max pooling function - * @param[in] Im_in pointer to input tensor - * @param[in] dim_im_in input tensor dimension - * @param[in] ch_im_in number of input tensor channels - * @param[in] dim_kernel filter kernel size - * @param[in] padding padding sizes - * @param[in] stride convolution stride - * @param[in] dim_im_out output tensor dimension - * @param[in,out] bufferA pointer to buffer space for input - * @param[in,out] Im_out pointer to output tensor - * @return none. - * - */ - -void arm_maxpool_q7_HWC(q7_t *Im_in, - const uint16_t dim_im_in, - const uint16_t ch_im_in, - const uint16_t dim_kernel, - const uint16_t padding, - const uint16_t stride, - const uint16_t dim_im_out, - q7_t *bufferA, - q7_t *Im_out); - -/** - * @brief Q7 average pooling function - * @param[in] Im_in pointer to input tensor - * @param[in] dim_im_in input tensor dimension - * @param[in] ch_im_in number of input tensor channels - * @param[in] dim_kernel filter kernel size - * @param[in] padding padding sizes - * @param[in] stride convolution stride - * @param[in] dim_im_out output tensor dimension - * @param[in,out] bufferA pointer to buffer space for input - * @param[in,out] Im_out pointer to output tensor - * @return none. - * - */ - -void arm_avepool_q7_HWC(q7_t *Im_in, - const uint16_t dim_im_in, - const uint16_t ch_im_in, - const uint16_t dim_kernel, - const uint16_t padding, - const uint16_t stride, - const uint16_t dim_im_out, - q7_t *bufferA, - q7_t *Im_out); - -/** - * @brief s8 average pooling function. - * - * @param[in, out] ctx Function context (e.g. temporary buffer). Check the function - * definition file to see if an additional buffer is required. - * Optional function {API}_get_buffer_size() provides the buffer - * size if an additional buffer is required. - * @param[in] pool_params Pooling parameters - * @param[in] input_dims Input (activation) tensor dimensions. Format: [H, W, C_IN] - * Argument 'N' is not used. - * @param[in] input_data Input (activation) data pointer. Data type: int8 - * @param[in] filter_dims Filter tensor dimensions. Format: [H, W] - * Argument N and C are not used. - * @param[in] output_dims Output tensor dimensions. Format: [H, W, C_OUT] - * Argument N is not used. - * C_OUT equals C_IN. - * @param[in, out] output_data Output data pointer. Data type: int8 - * @return The function returns - * <code>ARM_MATH_SUCCESS</code> - Successful operation - * - * @details - * - Supported Framework: TensorFlow Lite - * - */ -arm_status arm_avgpool_s8(const cmsis_nn_context *ctx, - const cmsis_nn_pool_params *pool_params, - const cmsis_nn_dims *input_dims, - const q7_t *input_data, - const cmsis_nn_dims *filter_dims, - const cmsis_nn_dims *output_dims, - q7_t *output_data); - -/** - * @brief Get the required buffer size for S8 average pooling function - * @param[in] dim_dst_width output tensor dimension - * @param[in] ch_src number of input tensor channels - * @return The function returns required buffer size in bytes - * - */ -int32_t arm_avgpool_s8_get_buffer_size(const int dim_dst_width, const int ch_src); - -/** - * @brief s16 average pooling function. - * - * @param[in, out] ctx Function context (e.g. temporary buffer). Check the function - * definition file to see if an additional buffer is required. - * Optional function {API}_get_buffer_size() provides the buffer - * size if an additional buffer is required. - * @param[in] pool_params Pooling parameters - * @param[in] input_dims Input (activation) tensor dimensions. Format: [H, W, C_IN] - * Argument 'N' is not used. - * @param[in] input_data Input (activation) data pointer. Data type: int16 - * @param[in] filter_dims Filter tensor dimensions. Format: [H, W] - * Argument N and C are not used. - * @param[in] output_dims Output tensor dimensions. Format: [H, W, C_OUT] - * Argument N is not used. - * C_OUT equals C_IN. - * @param[in, out] output_data Output data pointer. Data type: int16 - * @return The function returns - * <code>ARM_MATH_SUCCESS</code> - Successful operation - * - * @details - * - Supported Framework: TensorFlow Lite - * - */ -arm_status arm_avgpool_s16(const cmsis_nn_context *ctx, - const cmsis_nn_pool_params *pool_params, - const cmsis_nn_dims *input_dims, - const int16_t *input_data, - const cmsis_nn_dims *filter_dims, - const cmsis_nn_dims *output_dims, - int16_t *output_data); - -/** - * @brief Get the required buffer size for S16 average pooling function - * @param[in] dim_dst_width output tensor dimension - * @param[in] ch_src number of input tensor channels - * @return The function returns required buffer size in bytes - * - */ -int32_t arm_avgpool_s16_get_buffer_size(const int dim_dst_width, const int ch_src); - -/** - * @brief s8 max pooling function. - * - * @param[in, out] ctx Function context (e.g. temporary buffer). Check the function - * definition file to see if an additional buffer is required. - * Optional function {API}_get_buffer_size() provides the buffer - * size if an additional buffer is required. - * @param[in] pool_params Pooling parameters - * @param[in] input_dims Input (activation) tensor dimensions. Format: [H, W, C_IN] - * Argument 'N' is not used. - * @param[in] input_data Input (activation) data pointer. The input tensor must not - * overlap with the output tensor. Data type: int8 - * @param[in] filter_dims Filter tensor dimensions. Format: [H, W] - * Argument N and C are not used. - * @param[in] output_dims Output tensor dimensions. Format: [H, W, C_OUT] - * Argument N is not used. - * C_OUT equals C_IN. - * @param[in, out] output_data Output data pointer. Data type: int8 - * @return The function returns - * <code>ARM_MATH_SUCCESS</code> - Successful operation - * - * @details - * - Supported Framework: TensorFlow Lite - * - */ -arm_status arm_max_pool_s8(const cmsis_nn_context *ctx, - const cmsis_nn_pool_params *pool_params, - const cmsis_nn_dims *input_dims, - const q7_t *input_data, - const cmsis_nn_dims *filter_dims, - const cmsis_nn_dims *output_dims, - q7_t *output_data); - -/** - * @brief s16 max pooling function. - * - * @param[in, out] ctx Function context (e.g. temporary buffer). Check the function - * definition file to see if an additional buffer is required. - * Optional function {API}_get_buffer_size() provides the buffer - * size if an additional buffer is required. - * @param[in] pool_params Pooling parameters - * @param[in] input_dims Input (activation) tensor dimensions. Format: [H, W, C_IN] - * Argument 'N' is not used. - * @param[in] src Input (activation) data pointer. The input tensor must not - * overlap with the output tensor. Data type: int16 - * @param[in] filter_dims Filter tensor dimensions. Format: [H, W] - * Argument N and C are not used. - * @param[in] output_dims Output tensor dimensions. Format: [H, W, C_OUT] - * Argument N is not used. - * C_OUT equals C_IN. - * @param[in, out] dst Output data pointer. Data type: int16 - * @return The function returns - * <code>ARM_MATH_SUCCESS</code> - Successful operation - * - * @details - * - Supported Framework: TensorFlow Lite - * - */ -arm_status arm_max_pool_s16(const cmsis_nn_context *ctx, - const cmsis_nn_pool_params *pool_params, - const cmsis_nn_dims *input_dims, - const int16_t *src, - const cmsis_nn_dims *filter_dims, - const cmsis_nn_dims *output_dims, - int16_t *dst); - -/** - * @defgroup Softmax Softmax Functions - * - * EXP(2) based softmax functions. - * - */ - -/** - * @brief Q7 softmax function - * @param[in] vec_in pointer to input vector - * @param[in] dim_vec input vector dimension - * @param[out] p_out pointer to output vector - * - * @note This function is an optimized version which is not bit-accurate with - * TensorFlow Lite's kernel - * - */ - -void arm_softmax_q7(const q7_t *vec_in, const uint16_t dim_vec, q7_t *p_out); - -/** - * @brief Q7 softmax function with batch parameter - * @param[in] vec_in pointer to input vector - * @param[in] nb_batches number of batches - * @param[in] dim_vec input vector dimension - * @param[out] p_out pointer to output vector - * @return none. - * - * @note This function is an optimized version which is not bit-accurate with - * TensorFlow Lite's kernel - * - */ - -void arm_softmax_with_batch_q7(const q7_t *vec_in, const uint16_t nb_batches, const uint16_t dim_vec, q7_t *p_out); -/** - * @brief Q15 softmax function - * @param[in] vec_in pointer to input vector - * @param[in] dim_vec input vector dimension - * @param[out] p_out pointer to output vector - * @return none. - * - * @note This function is an optimized version which is not bit-accurate with - * TensorFlow Lite's kernel - * - */ - -void arm_softmax_q15(const q15_t *vec_in, const uint16_t dim_vec, q15_t *p_out); - -/** - * @brief S8 softmax function - * @param[in] input Pointer to the input tensor - * @param[in] num_rows Number of rows in the input tensor - * @param[in] row_size Number of elements in each input row - * @param[in] mult Input quantization multiplier - * @param[in] shift Input quantization shift within the range [0, 31] - * @param[in] diff_min Minimum difference with max in row. Used to check if - * the quantized exponential operation can be performed - * @param[out] output Pointer to the output tensor - * - * @note Supported framework: TensorFlow Lite micro (bit-accurate) - * - */ -void arm_softmax_s8(const int8_t *input, - const int32_t num_rows, - const int32_t row_size, - const int32_t mult, - const int32_t shift, - const int32_t diff_min, - int8_t *output); - -/** - * @brief S8 to s16 softmax function - * @param[in] input Pointer to the input tensor - * @param[in] num_rows Number of rows in the input tensor - * @param[in] row_size Number of elements in each input row - * @param[in] mult Input quantization multiplier - * @param[in] shift Input quantization shift within the range [0, 31] - * @param[in] diff_min Minimum difference with max in row. Used to check if - * the quantized exponential operation can be performed - * @param[out] output Pointer to the output tensor - * - * @note Supported framework: TensorFlow Lite micro (bit-accurate) - * - */ -void arm_softmax_s8_s16(const int8_t *input, - const int32_t num_rows, - const int32_t row_size, - const int32_t mult, - const int32_t shift, - const int32_t diff_min, - int16_t *output); - -/** - * @brief S16 softmax function - * @param[in] input Pointer to the input tensor - * @param[in] num_rows Number of rows in the input tensor - * @param[in] row_size Number of elements in each input row - * @param[in] mult Input quantization multiplier - * @param[in] shift Input quantization shift within the range [0, 31] - * @param[in] softmax_params Softmax s16 layer parameters with two pointers to LUTs speficied below. - * For indexing the high 9 bits are used and 7 remaining for interpolation. - * That means 512 entries for the 9-bit indexing and 1 extra for interpolation, i.e. 513 - * values for each LUT. - * - Lookup table for exp(x), where x uniform distributed between [-10.0 , 0.0] - * - Lookup table for 1 / (1 + x), where x uniform distributed between [0.0 , 1.0] - * @param[out] output Pointer to the output tensor - * @return The function returns - * <code>ARM_MATH_ARGUMENT_ERROR</code> if LUTs are NULL - * <code>ARM_MATH_SUCCESS</code> - Successful operation - * - * @note Supported framework: TensorFlow Lite micro (bit-accurate) - * - */ -arm_status arm_softmax_s16(const int16_t *input, - const int32_t num_rows, - const int32_t row_size, - const int32_t mult, - const int32_t shift, - const cmsis_nn_softmax_lut_s16 *softmax_params, - int16_t *output); - -/** - * @brief U8 softmax function - * @param[in] input Pointer to the input tensor - * @param[in] num_rows Number of rows in the input tensor - * @param[in] row_size Number of elements in each input row - * @param[in] mult Input quantization multiplier - * @param[in] shift Input quantization shift within the range [0, 31] - * @param[in] diff_min Minimum difference with max in row. Used to check if - * the quantized exponential operation can be performed - * @param[out] output Pointer to the output tensor - * - * @note Supported framework: TensorFlow Lite micro (bit-accurate) - * - */ - -void arm_softmax_u8(const uint8_t *input, - const int32_t num_rows, - const int32_t row_size, - const int32_t mult, - const int32_t shift, - const int32_t diff_min, - uint8_t *output); - -/** - * @brief uint8 depthwise convolution function with asymmetric quantization - * Unless specified otherwise, arguments are mandatory. - * - * @param[in] input Pointer to input tensor - * @param[in] input_x Width of input tensor - * @param[in] input_y Height of input tensor - * @param[in] input_ch Channels in input tensor - * @param[in] kernel Pointer to kernel weights - * @param[in] kernel_x Width of kernel - * @param[in] kernel_y Height of kernel - * @param[in] ch_mult Number of channel multiplier - * @param[in] pad_x Padding sizes x - * @param[in] pad_y Padding sizes y - * @param[in] stride_x stride along the width - * @param[in] stride_y stride along the height - * @param[in] dilation_x Dilation along width. Not used and intended for future enhancement. - * @param[in] dilation_y Dilation along height. Not used and intended for future enhancement. - * @param[in] bias Pointer to optional bias values. If no bias is - * availble, NULL is expected - * @param[in] input_offset Input tensor zero offset - * @param[in] filter_offset Kernel tensor zero offset - * @param[in] output_offset Output tensor zero offset - * @param[in,out] output Pointer to output tensor - * @param[in] output_x Width of output tensor - * @param[in] output_y Height of output tensor - * @param[in] output_activation_min Minimum value to clamp the output to. Range : {0, 255} - * @param[in] output_activation_max Minimum value to clamp the output to. Range : {0, 255} - * @param[in] out_shift Amount of right-shift for output - * @param[in] out_mult Output multiplier for requantization - * @return The function returns the following - * <code>ARM_MATH_SUCCESS</code> - Successful operation - * - */ -arm_status arm_depthwise_conv_u8_basic_ver1(const uint8_t *input, - const uint16_t input_x, - const uint16_t input_y, - const uint16_t input_ch, - const uint8_t *kernel, - const uint16_t kernel_x, - const uint16_t kernel_y, - const int16_t ch_mult, - const int16_t pad_x, - const int16_t pad_y, - const int16_t stride_x, - const int16_t stride_y, - const int16_t dilation_x, - const int16_t dilation_y, - const int32_t *bias, - const int32_t input_offset, - const int32_t filter_offset, - const int32_t output_offset, - uint8_t *output, - const uint16_t output_x, - const uint16_t output_y, - const int32_t output_activation_min, - const int32_t output_activation_max, - const int32_t out_shift, - const int32_t out_mult); - -/** - * @defgroup Reshape Reshape Functions - * - */ - -/** - * @brief Reshape a s8 vector into another with different shape - * @param[in] input points to the s8 input vector - * @param[out] output points to the s8 output vector - * @param[in] total_size total size of the input and output vectors in bytes - * - * @note The output is expected to be in a memory area that does not overlap with the input's - * - */ -void arm_reshape_s8(const int8_t *input, int8_t *output, const uint32_t total_size); - -/** - * @defgroup Concatenation Concatenation Functions - * - */ - -/** - * @brief int8/uint8 concatenation function to be used for concatenating N-tensors along the X axis - * This function should be called for each input tensor to concatenate. The argument offset_x - * will be used to store the input tensor in the correct position in the output tensor - * - * i.e. offset_x = 0 - * for(i = 0 i < num_input_tensors; ++i) - * { - * arm_concatenation_s8_x(&input[i], ..., &output, ..., ..., offset_x) - * offset_x += input_x[i] - * } - * - * This function assumes that the output tensor has: - * -# The same height of the input tensor - * -# The same number of channels of the input tensor - * -# The same batch size of the input tensor - * - * Unless specified otherwise, arguments are mandatory. - * - * @note This function, data layout independent, can be used to concatenate either int8 or uint8 tensors because it - * does not involve any arithmetic operation - * - * @param[in] input Pointer to input tensor. Input tensor must not overlap with the output tensor. - * @param[in] input_x Width of input tensor - * @param[in] input_y Height of input tensor - * @param[in] input_z Channels in input tensor - * @param[in] input_w Batch size in input tensor - * @param[out] output Pointer to output tensor. Expected to be at least - * (input_x * input_y * input_z * input_w) + offset_x - * bytes. - * @param[in] output_x Width of output tensor - * @param[in] offset_x The offset (in number of elements) on the X axis to start concatenating the input tensor - * It is user responsibility to provide the correct value - * - * <b> Input constraints</b> - * offset_x is less than output_x - * - */ -void arm_concatenation_s8_x(const int8_t *input, - const uint16_t input_x, - const uint16_t input_y, - const uint16_t input_z, - const uint16_t input_w, - int8_t *output, - const uint16_t output_x, - const uint32_t offset_x); - -/** - * @brief int8/uint8 concatenation function to be used for concatenating N-tensors along the Y axis - * This function should be called for each input tensor to concatenate. The argument offset_y - * will be used to store the input tensor in the correct position in the output tensor - * - * i.e. offset_y = 0 - * for(i = 0 i < num_input_tensors; ++i) - * { - * arm_concatenation_s8_y(&input[i], ..., &output, ..., ..., offset_y) - * offset_y += input_y[i] - * } - * - * This function assumes that the output tensor has: - * -# The same width of the input tensor - * -# The same number of channels of the input tensor - * -# The same batch size of the input tensor - * - * Unless specified otherwise, arguments are mandatory. - * - * @note This function, data layout independent, can be used to concatenate either int8 or uint8 tensors because it - * does not involve any arithmetic operation - * - * @param[in] input Pointer to input tensor. Input tensor must not overlap with the output tensor. - * @param[in] input_x Width of input tensor - * @param[in] input_y Height of input tensor - * @param[in] input_z Channels in input tensor - * @param[in] input_w Batch size in input tensor - * @param[out] output Pointer to output tensor. Expected to be at least - * (input_z * input_w * input_x * input_y) + offset_y - * bytes. - * @param[in] output_y Height of output tensor - * @param[in] offset_y The offset on the Y axis to start concatenating the input tensor - * It is user responsibility to provide the correct value - * - * <b> Input constraints</b> - * offset_y is less than output_y - * - */ -void arm_concatenation_s8_y(const int8_t *input, - const uint16_t input_x, - const uint16_t input_y, - const uint16_t input_z, - const uint16_t input_w, - int8_t *output, - const uint16_t output_y, - const uint32_t offset_y); - -/** - * @brief int8/uint8 concatenation function to be used for concatenating N-tensors along the Z axis - * This function should be called for each input tensor to concatenate. The argument offset_z - * will be used to store the input tensor in the correct position in the output tensor - * - * i.e. offset_z = 0 - * for(i = 0 i < num_input_tensors; ++i) - * { - * arm_concatenation_s8_z(&input[i], ..., &output, ..., ..., offset_z) - * offset_z += input_z[i] - * } - * - * This function assumes that the output tensor has: - * -# The same width of the input tensor - * -# The same height of the input tensor - * -# The same batch size of the input tensor - * - * Unless specified otherwise, arguments are mandatory. - * - * @note This function, data layout independent, can be used to concatenate either int8 or uint8 tensors because it - * does not involve any arithmetic operation - * - * @param[in] input Pointer to input tensor. Input tensor must not overlap with output tensor. - * @param[in] input_x Width of input tensor - * @param[in] input_y Height of input tensor - * @param[in] input_z Channels in input tensor - * @param[in] input_w Batch size in input tensor - * @param[out] output Pointer to output tensor. Expected to be at least - * (input_x * input_y * input_z * input_w) + offset_z - * bytes. - * @param[in] output_z Channels in output tensor - * @param[in] offset_z The offset on the Z axis to start concatenating the input tensor - * It is user responsibility to provide the correct value - * - * <b> Input constraints</b> - * offset_z is less than output_z - * - */ -void arm_concatenation_s8_z(const int8_t *input, - const uint16_t input_x, - const uint16_t input_y, - const uint16_t input_z, - const uint16_t input_w, - int8_t *output, - const uint16_t output_z, - const uint32_t offset_z); - -/** - * @brief int8/uint8 concatenation function to be used for concatenating N-tensors along the W axis (Batch size) - * This function should be called for each input tensor to concatenate. The argument offset_w - * will be used to store the input tensor in the correct position in the output tensor - * - * i.e. offset_w = 0 - * for(i = 0 i < num_input_tensors; ++i) - * { - * arm_concatenation_s8_w(&input[i], ..., &output, ..., ..., offset_w) - * offset_w += input_w[i] - * } - * - * This function assumes that the output tensor has: - * -# The same width of the input tensor - * -# The same height of the input tensor - * -# The same number o channels of the input tensor - * - * Unless specified otherwise, arguments are mandatory. - * - * @note This function, data layout independent, can be used to concatenate either int8 or uint8 tensors because it - * does not involve any arithmetic operation - * - * @param[in] input Pointer to input tensor - * @param[in] input_x Width of input tensor - * @param[in] input_y Height of input tensor - * @param[in] input_z Channels in input tensor - * @param[in] input_w Batch size in input tensor - * @param[out] output Pointer to output tensor. Expected to be at least - * input_x * input_y * input_z * input_w - * bytes. - * @param[in] offset_w The offset on the W axis to start concatenating the input tensor - * It is user responsibility to provide the correct value - * - */ -void arm_concatenation_s8_w(const int8_t *input, - const uint16_t input_x, - const uint16_t input_y, - const uint16_t input_z, - const uint16_t input_w, - int8_t *output, - const uint32_t offset_w); -/** - * @defgroup SVDF SVDF Layer Functions - * - */ - -/** - * @brief s8 SVDF function with 8 bit state tensor and 8 bit time weights - * - * @param[in] input_ctx Temporary scratch buffer - * @param[in] output_ctx Temporary output scratch buffer - * @param[in] svdf_params SVDF Parameters - * Range of svdf_params->input_offset : [-128, 127] - * Range of svdf_params->output_offset : [-128, 127] - * @param[in] input_quant_params Input quantization parameters - * @param[in] output_quant_params Output quantization parameters - * @param[in] input_dims Input tensor dimensions - * @param[in] input_data Pointer to input tensor - * @param[in] state_dims State tensor dimensions - * @param[in] state_data Pointer to state tensor - * @param[in] weights_feature_dims Weights (feature) tensor dimensions - * @param[in] weights_feature_data Pointer to the weights (feature) tensor - * @param[in] weights_time_dims Weights (time) tensor dimensions - * @param[in] weights_time_data Pointer to the weights (time) tensor - * @param[in] bias_dims Bias tensor dimensions - * @param[in] bias_data Pointer to bias tensor - * @param[in] output_dims Output tensor dimensions - * @param[out] output_data Pointer to the output tensor - * - * @return The function returns <code>ARM_MATH_SUCCESS</code> - * - * @details - * 1. Supported framework: TensorFlow Lite micro - * 2. q7 is used as data type eventhough it is s8 data. It is done so to be consistent with existing APIs. - * - */ -arm_status arm_svdf_s8(const cmsis_nn_context *input_ctx, - const cmsis_nn_context *output_ctx, - const cmsis_nn_svdf_params *svdf_params, - const cmsis_nn_per_tensor_quant_params *input_quant_params, - const cmsis_nn_per_tensor_quant_params *output_quant_params, - const cmsis_nn_dims *input_dims, - const q7_t *input_data, - const cmsis_nn_dims *state_dims, - q7_t *state_data, - const cmsis_nn_dims *weights_feature_dims, - const q7_t *weights_feature_data, - const cmsis_nn_dims *weights_time_dims, - const q7_t *weights_time_data, - const cmsis_nn_dims *bias_dims, - const q31_t *bias_data, - const cmsis_nn_dims *output_dims, - q7_t *output_data); - -/** - * @brief s8 SVDF function with 16 bit state tensor and 16 bit time weights - * - * @param[in] input_ctx Temporary scratch buffer - * @param[in] output_ctx Temporary output scratch buffer - * @param[in] svdf_params SVDF Parameters - * Range of svdf_params->input_offset : [-128, 127] - * Range of svdf_params->output_offset : [-128, 127] - * @param[in] input_quant_params Input quantization parameters - * @param[in] output_quant_params Output quantization parameters - * @param[in] input_dims Input tensor dimensions - * @param[in] input_data Pointer to input tensor - * @param[in] state_dims State tensor dimensions - * @param[in] state_data Pointer to state tensor - * @param[in] weights_feature_dims Weights (feature) tensor dimensions - * @param[in] weights_feature_data Pointer to the weights (feature) tensor - * @param[in] weights_time_dims Weights (time) tensor dimensions - * @param[in] weights_time_data Pointer to the weights (time) tensor - * @param[in] bias_dims Bias tensor dimensions - * @param[in] bias_data Pointer to bias tensor - * @param[in] output_dims Output tensor dimensions - * @param[out] output_data Pointer to the output tensor - * - * @return The function returns <code>ARM_MATH_SUCCESS</code> - * - * @details - * 1. Supported framework: TensorFlow Lite micro - * 2. q7 is used as data type eventhough it is s8 data. It is done so to be consistent with existing APIs. - * - */ -arm_status arm_svdf_state_s16_s8(const cmsis_nn_context *input_ctx, - const cmsis_nn_context *output_ctx, - const cmsis_nn_svdf_params *svdf_params, - const cmsis_nn_per_tensor_quant_params *input_quant_params, - const cmsis_nn_per_tensor_quant_params *output_quant_params, - const cmsis_nn_dims *input_dims, - const q7_t *input_data, - const cmsis_nn_dims *state_dims, - q15_t *state_data, - const cmsis_nn_dims *weights_feature_dims, - const q7_t *weights_feature_data, - const cmsis_nn_dims *weights_time_dims, - const q15_t *weights_time_data, - const cmsis_nn_dims *bias_dims, - const q31_t *bias_data, - const cmsis_nn_dims *output_dims, - q7_t *output_data); - -#ifdef __cplusplus -} -#endif - -#endif +/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project: CMSIS NN Library
+ * Title: arm_nnfunctions.h
+ * Description: Public header file for CMSIS NN Library
+ *
+ * $Date: 13. July 2018
+ * $Revision: V.1.0.0
+ *
+ * Target Processor: Cortex-M cores
+ * -------------------------------------------------------------------- */
+
+/**
+ \mainpage CMSIS NN Software Library
+ *
+ * Introduction
+ * ------------
+ *
+ * This user manual describes the CMSIS NN software library,
+ * a collection of efficient neural network kernels developed to maximize the
+ * performance and minimize the memory footprint of neural networks on Cortex-M processor cores.
+ *
+ * The library is divided into a number of functions each covering a specific category:
+ * - Neural Network Convolution Functions
+ * - Neural Network Activation Functions
+ * - Fully-connected Layer Functions
+ * - Neural Network Pooling Functions
+ * - Softmax Functions
+ * - Neural Network Support Functions
+ *
+ * The library has separate functions for operating on different weight and activation data
+ * types including 8-bit integers (q7_t) and 16-bit integers (q15_t). The descrition of the
+ * kernels are included in the function description. The implementation details are also
+ * described in this paper [1].
+ *
+ * Block Diagram
+ * --------
+ * \image html CMSIS-NN-OVERVIEW.PNG
+ *
+ * Examples
+ * --------
+ *
+ * The library ships with a number of examples which demonstrate how to use the library functions.
+ *
+ * Pre-processor Macros
+ * ------------
+ *
+ * Each library project have differant pre-processor macros.
+ *
+ * - ARM_MATH_DSP:
+ *
+ * Define macro ARM_MATH_DSP, If the silicon supports DSP instructions.
+ *
+ * - ARM_MATH_BIG_ENDIAN:
+ *
+ * Define macro ARM_MATH_BIG_ENDIAN to build the library for big endian targets. By default library builds for little endian targets.
+ *
+ * - ARM_NN_TRUNCATE:
+ *
+ * Define macro ARM_NN_TRUNCATE to use floor instead of round-to-the-nearest-int for the computation.
+ *
+ * Copyright Notice
+ * ------------
+ *
+ * Copyright (C) 2010-2018 Arm Limited. All rights reserved.
+ *
+ * [1] CMSIS-NN: Efficient Neural Network Kernels for Arm Cortex-M CPUs https://arxiv.org/abs/1801.06601
+ */
+
+/**
+ * @defgroup groupNN Neural Network Functions
+ * These functions perform basic operations for neural network layers.
+ */
+
+#ifndef _ARM_NNFUNCTIONS_H
+#define _ARM_NNFUNCTIONS_H
+
+#include "arm_nnsupportfunctions.h"
+#include "arm_nn_tables.h"
+
+#define USE_INTRINSIC
+
+//#define ARM_NN_TRUNCATE /* This config the rounding model to floor or round to the nearest int */
+
+#ifdef __cplusplus
+extern "C"
+{
+#endif
+
+/**
+ * @defgroup NNConv Neural Network Convolution Functions
+ *
+ * Perform convolution layer
+ *
+ * The convolution is implemented in 2 steps: im2col and GEMM
+ *
+ * im2col is a process of converting each patch of image data into
+ * a column. After im2col, the convolution is computed as matrix-matrix
+ * multiplication.
+ *
+ * To reduce the memory footprint, the im2col is performed partially.
+ * Each iteration, only a few column (i.e., patches) are generated and
+ * computed with GEMM kernels similar to CMSIS-DSP arm_mat_mult functions.
+ *
+ */
+
+ /**
+ * @brief Basic Q7 convolution function
+ * @param[in] Im_in pointer to input tensor
+ * @param[in] dim_im_in input tensor dimention
+ * @param[in] ch_im_in number of input tensor channels
+ * @param[in] wt pointer to kernel weights
+ * @param[in] ch_im_out number of filters, i.e., output tensor channels
+ * @param[in] dim_kernel filter kernel size
+ * @param[in] padding padding sizes
+ * @param[in] stride convolution stride
+ * @param[in] bias pointer to bias
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in,out] Im_out pointer to output tensor
+ * @param[in] dim_im_out output tensor dimension
+ * @param[in,out] bufferA pointer to buffer space for input
+ * @param[in,out] bufferB pointer to buffer space for output
+ * @return The function returns <code>ARM_MATH_SUCCESS</code>
+ *
+ */
+
+ arm_status arm_convolve_HWC_q7_basic(const q7_t * Im_in,
+ const uint16_t dim_im_in,
+ const uint16_t ch_im_in,
+ const q7_t * wt,
+ const uint16_t ch_im_out,
+ const uint16_t dim_kernel,
+ const uint16_t padding,
+ const uint16_t stride,
+ const q7_t * bias,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ q7_t * Im_out,
+ const uint16_t dim_im_out,
+ q15_t * bufferA,
+ q7_t * bufferB);
+
+ /**
+ * @brief Basic Q7 convolution function (non-sqaure shape)
+ * @param[in] Im_in pointer to input tensor
+ * @param[in] dim_im_in_x input tensor dimention x
+ * @param[in] dim_im_in_y input tensor dimention y
+ * @param[in] ch_im_in number of input tensor channels
+ * @param[in] wt pointer to kernel weights
+ * @param[in] ch_im_out number of filters, i.e., output tensor channels
+ * @param[in] dim_kernel_x filter kernel size x
+ * @param[in] dim_kernel_y filter kernel size y
+ * @param[in] padding_x padding size x
+ * @param[in] padding_y padding size y
+ * @param[in] stride_x convolution stride x
+ * @param[in] stride_y convolution stride y
+ * @param[in] bias pointer to bias
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in,out] Im_out pointer to output tensor
+ * @param[in] dim_im_out_x output tensor dimension x
+ * @param[in] dim_im_out_y output tensor dimension y
+ * @param[in,out] bufferA pointer to buffer space for input
+ * @param[in,out] bufferB pointer to buffer space for output
+ * @return The function returns <code>ARM_MATH_SUCCESS</code>
+ */
+
+ arm_status arm_convolve_HWC_q7_basic_nonsquare(const q7_t * Im_in,
+ const uint16_t dim_im_in_x,
+ const uint16_t dim_im_in_y,
+ const uint16_t ch_im_in,
+ const q7_t * wt,
+ const uint16_t ch_im_out,
+ const uint16_t dim_kernel_x,
+ const uint16_t dim_kernel_y,
+ const uint16_t padding_x,
+ const uint16_t padding_y,
+ const uint16_t stride_x,
+ const uint16_t stride_y,
+ const q7_t * bias,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ q7_t * Im_out,
+ const uint16_t dim_im_out_x,
+ const uint16_t dim_im_out_y,
+ q15_t * bufferA,
+ q7_t * bufferB);
+
+ /**
+ * @brief Basic Q15 convolution function
+ * @param[in] Im_in pointer to input tensor
+ * @param[in] dim_im_in input tensor dimention
+ * @param[in] ch_im_in number of input tensor channels
+ * @param[in] wt pointer to kernel weights
+ * @param[in] ch_im_out number of filters, i.e., output tensor channels
+ * @param[in] dim_kernel filter kernel size
+ * @param[in] padding padding sizes
+ * @param[in] stride convolution stride
+ * @param[in] bias pointer to bias
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in,out] Im_out pointer to output tensor
+ * @param[in] dim_im_out output tensor dimension
+ * @param[in,out] bufferA pointer to buffer space for input
+ * @param[in,out] bufferB pointer to buffer space for output
+ * @return The function returns <code>ARM_MATH_SUCCESS</code>
+ *
+ */
+
+ arm_status arm_convolve_HWC_q15_basic(const q15_t * Im_in,
+ const uint16_t dim_im_in,
+ const uint16_t ch_im_in,
+ const q15_t * wt,
+ const uint16_t ch_im_out,
+ const uint16_t dim_kernel,
+ const uint16_t padding,
+ const uint16_t stride,
+ const q15_t * bias,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ q15_t * Im_out,
+ const uint16_t dim_im_out,
+ q15_t * bufferA,
+ q7_t * bufferB);
+
+ /**
+ * @brief Fast Q7 convolution function
+ * @param[in] Im_in pointer to input tensor
+ * @param[in] dim_im_in input tensor dimention
+ * @param[in] ch_im_in number of input tensor channels
+ * @param[in] wt pointer to kernel weights
+ * @param[in] ch_im_out number of filters, i.e., output tensor channels
+ * @param[in] dim_kernel filter kernel size
+ * @param[in] padding padding sizes
+ * @param[in] stride convolution stride
+ * @param[in] bias pointer to bias
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in,out] Im_out pointer to output tensor
+ * @param[in] dim_im_out output tensor dimension
+ * @param[in,out] bufferA pointer to buffer space for input
+ * @param[in,out] bufferB pointer to buffer space for output
+ * @return The function returns either
+ * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
+ *
+ * This function is the version with full list of optimization tricks, but with
+ * some contraints:
+ * ch_im_in is multiple of 4
+ * ch_im_out is multiple of 2
+ */
+
+ arm_status arm_convolve_HWC_q7_fast(const q7_t * Im_in,
+ const uint16_t dim_im_in,
+ const uint16_t ch_im_in,
+ const q7_t * wt,
+ const uint16_t ch_im_out,
+ const uint16_t dim_kernel,
+ const uint16_t padding,
+ const uint16_t stride,
+ const q7_t * bias,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ q7_t * Im_out,
+ const uint16_t dim_im_out,
+ q15_t * bufferA,
+ q7_t * bufferB);
+
+ /**
+ * @brief Fast Q7 convolution function (non-sqaure shape)
+ * @param[in] Im_in pointer to input tensor
+ * @param[in] dim_im_in_x input tensor dimention x
+ * @param[in] dim_im_in_y input tensor dimention y
+ * @param[in] ch_im_in number of input tensor channels
+ * @param[in] wt pointer to kernel weights
+ * @param[in] ch_im_out number of filters, i.e., output tensor channels
+ * @param[in] dim_kernel_x filter kernel size x
+ * @param[in] dim_kernel_y filter kernel size y
+ * @param[in] padding_x padding size x
+ * @param[in] padding_y padding size y
+ * @param[in] stride_x convolution stride x
+ * @param[in] stride_y convolution stride y
+ * @param[in] bias pointer to bias
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in,out] Im_out pointer to output tensor
+ * @param[in] dim_im_out_x output tensor dimension x
+ * @param[in] dim_im_out_y output tensor dimension y
+ * @param[in,out] bufferA pointer to buffer space for input
+ * @param[in,out] bufferB pointer to buffer space for output
+ * @return The function returns either
+ * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
+ *
+ * This function is the version with full list of optimization tricks, but with
+ * some contraints:
+ * ch_im_in is multiple of 4
+ * ch_im_out is multiple of 2
+ */
+
+ arm_status arm_convolve_HWC_q7_fast_nonsquare(const q7_t * Im_in,
+ const uint16_t dim_im_in_x,
+ const uint16_t dim_im_in_y,
+ const uint16_t ch_im_in,
+ const q7_t * wt,
+ const uint16_t ch_im_out,
+ const uint16_t dim_kernel_x,
+ const uint16_t dim_kernel_y,
+ const uint16_t padding_x,
+ const uint16_t padding_y,
+ const uint16_t stride_x,
+ const uint16_t stride_y,
+ const q7_t * bias,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ q7_t * Im_out,
+ const uint16_t dim_im_out_x,
+ const uint16_t dim_im_out_y,
+ q15_t * bufferA,
+ q7_t * bufferB);
+
+ /**
+ * @brief Fast Q7 version of 1x1 convolution (non-sqaure shape)
+ * @param[in] Im_in pointer to input tensor
+ * @param[in] dim_im_in_x input tensor dimention x
+ * @param[in] dim_im_in_y input tensor dimention y
+ * @param[in] ch_im_in number of input tensor channels
+ * @param[in] wt pointer to kernel weights
+ * @param[in] ch_im_out number of filters, i.e., output tensor channels
+ * @param[in] dim_kernel_x filter kernel size x
+ * @param[in] dim_kernel_y filter kernel size y
+ * @param[in] padding_x padding size x
+ * @param[in] padding_y padding size y
+ * @param[in] stride_x convolution stride x
+ * @param[in] stride_y convolution stride y
+ * @param[in] bias pointer to bias
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in,out] Im_out pointer to output tensor
+ * @param[in] dim_im_out_x output tensor dimension x
+ * @param[in] dim_im_out_y output tensor dimension y
+ * @param[in,out] bufferA pointer to buffer space for input
+ * @param[in,out] bufferB pointer to buffer space for output
+ * @return The function returns either
+ * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
+ *
+ * This function implement convolution with 1x1 kernel size (i.e., dim_kernel_x=1
+ * and dim_kernel_y=1). It can be used for
+ * second half of MobileNets after depthwise separable convolution.
+ *
+ * This function is the version with full list of optimization tricks, but with
+ * some contraints:
+ * ch_im_in is multiple of 4
+ * ch_im_out is multiple of 2
+ */
+ arm_status arm_convolve_1x1_HWC_q7_fast_nonsquare(const q7_t * Im_in,
+ const uint16_t dim_im_in_x,
+ const uint16_t dim_im_in_y,
+ const uint16_t ch_im_in,
+ const q7_t * wt,
+ const uint16_t ch_im_out,
+ const uint16_t dim_kernel_x,
+ const uint16_t dim_kernel_y,
+ const uint16_t padding_x,
+ const uint16_t padding_y,
+ const uint16_t stride_x,
+ const uint16_t stride_y,
+ const q7_t * bias,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ q7_t * Im_out,
+ const uint16_t dim_im_out_x,
+ const uint16_t dim_im_out_y,
+ q15_t * bufferA,
+ q7_t * bufferB);
+
+ /**
+ * @brief Q7 version of convolution for RGB image
+ * @param[in] Im_in pointer to input tensor
+ * @param[in] dim_im_in input tensor dimention
+ * @param[in] ch_im_in number of input tensor channels
+ * @param[in] wt pointer to kernel weights
+ * @param[in] ch_im_out number of filters, i.e., output tensor channels
+ * @param[in] dim_kernel filter kernel size
+ * @param[in] padding padding sizes
+ * @param[in] stride convolution stride
+ * @param[in] bias pointer to bias
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in,out] Im_out pointer to output tensor
+ * @param[in] dim_im_out output tensor dimension
+ * @param[in,out] bufferA pointer to buffer space for input
+ * @param[in,out] bufferB pointer to buffer space for output
+ * @return The function returns either
+ * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
+ *
+ * This kernel is written exclusively for convolution with ch_im_in
+ * equals 3. This applies on the first layer of CNNs which has input
+ * image with RGB format.
+ */
+
+ arm_status arm_convolve_HWC_q7_RGB(const q7_t * Im_in,
+ const uint16_t dim_im_in,
+ const uint16_t ch_im_in,
+ const q7_t * wt,
+ const uint16_t ch_im_out,
+ const uint16_t dim_kernel,
+ const uint16_t padding,
+ const uint16_t stride,
+ const q7_t * bias,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ q7_t * Im_out,
+ const uint16_t dim_im_out,
+ q15_t * bufferA,
+ q7_t * bufferB);
+
+ /**
+ * @brief Fast Q15 convolution function
+ * @param[in] Im_in pointer to input tensor
+ * @param[in] dim_im_in input tensor dimention
+ * @param[in] ch_im_in number of input tensor channels
+ * @param[in] wt pointer to kernel weights
+ * @param[in] ch_im_out number of filters, i.e., output tensor channels
+ * @param[in] dim_kernel filter kernel size
+ * @param[in] padding padding sizes
+ * @param[in] stride convolution stride
+ * @param[in] bias pointer to bias
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in,out] Im_out pointer to output tensor
+ * @param[in] dim_im_out output tensor dimension
+ * @param[in,out] bufferA pointer to buffer space for input
+ * @param[in,out] bufferB pointer to buffer space for output
+ * @return The function returns either
+ * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
+ *
+ * This function is the version with full list of optimization tricks, but with
+ * some contraints:
+ * ch_im_in is multiple of 2
+ * ch_im_out is multiple of 2
+ */
+
+ arm_status arm_convolve_HWC_q15_fast(const q15_t * Im_in,
+ const uint16_t dim_im_in,
+ const uint16_t ch_im_in,
+ const q15_t * wt,
+ const uint16_t ch_im_out,
+ const uint16_t dim_kernel,
+ const uint16_t padding,
+ const uint16_t stride,
+ const q15_t * bias,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ q15_t * Im_out,
+ const uint16_t dim_im_out,
+ q15_t * bufferA,
+ q7_t * bufferB);
+
+ /**
+ * @brief Fast Q15 convolution function (non-sqaure shape)
+ * @param[in] Im_in pointer to input tensor
+ * @param[in] dim_im_in_x input tensor dimention x
+ * @param[in] dim_im_in_y input tensor dimention y
+ * @param[in] ch_im_in number of input tensor channels
+ * @param[in] wt pointer to kernel weights
+ * @param[in] ch_im_out number of filters, i.e., output tensor channels
+ * @param[in] dim_kernel_x filter kernel size x
+ * @param[in] dim_kernel_y filter kernel size y
+ * @param[in] padding_x padding size x
+ * @param[in] padding_y padding size y
+ * @param[in] stride_x convolution stride x
+ * @param[in] stride_y convolution stride y
+ * @param[in] bias pointer to bias
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in,out] Im_out pointer to output tensor
+ * @param[in] dim_im_out_x output tensor dimension x
+ * @param[in] dim_im_out_y output tensor dimension y
+ * @param[in,out] bufferA pointer to buffer space for input
+ * @param[in,out] bufferB pointer to buffer space for output
+ * @return The function returns either
+ * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
+ *
+ * @details
+ *
+ * <b>Buffer size:</b>
+ *
+ * bufferA size: 2*ch_im_in*dim_kernel*dim_kernel
+ *
+ * bufferB size: 0
+ *
+ * <b>Input dimension constraints:</b>
+ *
+ * ch_im_in is multiple of 2
+ *
+ * ch_im_out is multipe of 2
+ *
+ */
+
+ arm_status
+ arm_convolve_HWC_q15_fast_nonsquare(const q15_t * Im_in,
+ const uint16_t dim_im_in_x,
+ const uint16_t dim_im_in_y,
+ const uint16_t ch_im_in,
+ const q15_t * wt,
+ const uint16_t ch_im_out,
+ const uint16_t dim_kernel_x,
+ const uint16_t dim_kernel_y,
+ const uint16_t padding_x,
+ const uint16_t padding_y,
+ const uint16_t stride_x,
+ const uint16_t stride_y,
+ const q15_t * bias,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ q15_t * Im_out,
+ const uint16_t dim_im_out_x,
+ const uint16_t dim_im_out_y,
+ q15_t * bufferA,
+ q7_t * bufferB);
+
+ /**
+ * @brief Q7 depthwise separable convolution function
+ * @param[in] Im_in pointer to input tensor
+ * @param[in] dim_im_in input tensor dimention
+ * @param[in] ch_im_in number of input tensor channels
+ * @param[in] wt pointer to kernel weights
+ * @param[in] ch_im_out number of filters, i.e., output tensor channels
+ * @param[in] dim_kernel filter kernel size
+ * @param[in] padding padding sizes
+ * @param[in] stride convolution stride
+ * @param[in] bias pointer to bias
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in,out] Im_out pointer to output tensor
+ * @param[in] dim_im_out output tensor dimension
+ * @param[in,out] bufferA pointer to buffer space for input
+ * @param[in,out] bufferB pointer to buffer space for output
+ * @return The function returns either
+ * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
+ *
+ * This function is the version with full list of optimization tricks, but with
+ * some contraints:
+ * ch_im_in is multiple of 2
+ * ch_im_out is multiple of 2
+ */
+
+ arm_status arm_depthwise_separable_conv_HWC_q7(const q7_t * Im_in,
+ const uint16_t dim_im_in,
+ const uint16_t ch_im_in,
+ const q7_t * wt,
+ const uint16_t ch_im_out,
+ const uint16_t dim_kernel,
+ const uint16_t padding,
+ const uint16_t stride,
+ const q7_t * bias,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ q7_t * Im_out,
+ const uint16_t dim_im_out,
+ q15_t * bufferA,
+ q7_t * bufferB);
+
+ /**
+ * @brief Q7 depthwise separable convolution function (non-square shape)
+ * @param[in] Im_in pointer to input tensor
+ * @param[in] dim_im_in_x input tensor dimention x
+ * @param[in] dim_im_in_y input tensor dimention y
+ * @param[in] ch_im_in number of input tensor channels
+ * @param[in] wt pointer to kernel weights
+ * @param[in] ch_im_out number of filters, i.e., output tensor channels
+ * @param[in] dim_kernel_x filter kernel size x
+ * @param[in] dim_kernel_y filter kernel size y
+ * @param[in] padding_x padding sizes x
+ * @param[in] padding_y padding sizes y
+ * @param[in] stride_x convolution stride x
+ * @param[in] stride_y convolution stride y
+ * @param[in] bias pointer to bias
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in,out] Im_out pointer to output tensor
+ * @param[in] dim_im_out_x output tensor dimension x
+ * @param[in] dim_im_out_y output tensor dimension y
+ * @param[in,out] bufferA pointer to buffer space for input
+ * @param[in,out] bufferB pointer to buffer space for output
+ * @return The function returns either
+ * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
+ *
+ * This function is the version with full list of optimization tricks, but with
+ * some contraints:
+ * ch_im_in is multiple of 2
+ * ch_im_out is multiple of 2
+ */
+ arm_status arm_depthwise_separable_conv_HWC_q7_nonsquare(const q7_t * Im_in,
+ const uint16_t dim_im_in_x,
+ const uint16_t dim_im_in_y,
+ const uint16_t ch_im_in,
+ const q7_t * wt,
+ const uint16_t ch_im_out,
+ const uint16_t dim_kernel_x,
+ const uint16_t dim_kernel_y,
+ const uint16_t padding_x,
+ const uint16_t padding_y,
+ const uint16_t stride_x,
+ const uint16_t stride_y,
+ const q7_t * bias,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ q7_t * Im_out,
+ const uint16_t dim_im_out_x,
+ const uint16_t dim_im_out_y,
+ q15_t * bufferA,
+ q7_t * bufferB);
+
+
+/**
+ * @defgroup FC Fully-connected Layer Functions
+ *
+ * Perform fully-connected layer
+ *
+ * Fully-connected layer is basically a matrix-vector multiplication
+ * with bias. The matrix is the weights and the input/output vectors
+ * are the activation values. Supported {weight, activation} precisions
+ * include {8-bit, 8-bit}, {16-bit, 16-bit}, and {8-bit, 16-bit}.
+ *
+ * Here we have two types of kernel functions. The basic function
+ * implements the function using regular GEMV approach. The opt functions
+ * operates with weights in interleaved formats.
+ *
+ */
+
+ /**
+ * @brief Q7 basic fully-connected layer function
+ * @param[in] pV pointer to input vector
+ * @param[in] pM pointer to matrix weights
+ * @param[in] dim_vec length of the vector
+ * @param[in] num_of_rows number of rows in weight matrix
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in] bias pointer to bias
+ * @param[in,out] pOut pointer to output vector
+ * @param[in,out] vec_buffer pointer to buffer space for input
+ * @return The function returns <code>ARM_MATH_SUCCESS</code>
+ *
+ */
+
+ arm_status arm_fully_connected_q7(const q7_t * pV,
+ const q7_t * pM,
+ const uint16_t dim_vec,
+ const uint16_t num_of_rows,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ const q7_t * bias,
+ q7_t * pOut,
+ q15_t * vec_buffer);
+
+ /**
+ * @brief Q7 opt fully-connected layer function
+ * @param[in] pV pointer to input vector
+ * @param[in] pM pointer to matrix weights
+ * @param[in] dim_vec length of the vector
+ * @param[in] num_of_rows number of rows in weight matrix
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in] bias pointer to bias
+ * @param[in,out] pOut pointer to output vector
+ * @param[in,out] vec_buffer pointer to buffer space for input
+ * @return The function returns <code>ARM_MATH_SUCCESS</code>
+ *
+ */
+
+ arm_status arm_fully_connected_q7_opt(const q7_t * pV,
+ const q7_t * pM,
+ const uint16_t dim_vec,
+ const uint16_t num_of_rows,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ const q7_t * bias,
+ q7_t * pOut,
+ q15_t * vec_buffer);
+
+ /**
+ * @brief Q15 basic fully-connected layer function
+ * @param[in] pV pointer to input vector
+ * @param[in] pM pointer to matrix weights
+ * @param[in] dim_vec length of the vector
+ * @param[in] num_of_rows number of rows in weight matrix
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in] bias pointer to bias
+ * @param[in,out] pOut pointer to output vector
+ * @param[in,out] vec_buffer pointer to buffer space for input
+ * @return The function returns <code>ARM_MATH_SUCCESS</code>
+ *
+ */
+
+ arm_status arm_fully_connected_q15(const q15_t * pV,
+ const q15_t * pM,
+ const uint16_t dim_vec,
+ const uint16_t num_of_rows,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ const q15_t * bias,
+ q15_t * pOut,
+ q15_t * vec_buffer);
+
+ /**
+ * @brief Q15 opt fully-connected layer function
+ * @param[in] pV pointer to input vector
+ * @param[in] pM pointer to matrix weights
+ * @param[in] dim_vec length of the vector
+ * @param[in] num_of_rows number of rows in weight matrix
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in] bias pointer to bias
+ * @param[in,out] pOut pointer to output vector
+ * @param[in,out] vec_buffer pointer to buffer space for input
+ * @return The function returns <code>ARM_MATH_SUCCESS</code>
+ *
+ */
+
+ arm_status arm_fully_connected_q15_opt(const q15_t * pV,
+ const q15_t * pM,
+ const uint16_t dim_vec,
+ const uint16_t num_of_rows,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ const q15_t * bias,
+ q15_t * pOut,
+ q15_t * vec_buffer);
+
+ /**
+ * @brief Mixed Q15-Q7 fully-connected layer function
+ * @param[in] pV pointer to input vector
+ * @param[in] pM pointer to matrix weights
+ * @param[in] dim_vec length of the vector
+ * @param[in] num_of_rows number of rows in weight matrix
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in] bias pointer to bias
+ * @param[in,out] pOut pointer to output vector
+ * @param[in,out] vec_buffer pointer to buffer space for input
+ * @return The function returns <code>ARM_MATH_SUCCESS</code>
+ *
+ */
+
+ arm_status arm_fully_connected_mat_q7_vec_q15(const q15_t * pV,
+ const q7_t * pM,
+ const uint16_t dim_vec,
+ const uint16_t num_of_rows,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ const q7_t * bias,
+ q15_t * pOut,
+ q15_t * vec_buffer);
+
+ /**
+ * @brief Mixed Q15-Q7 opt fully-connected layer function
+ * @param[in] pV pointer to input vector
+ * @param[in] pM pointer to matrix weights
+ * @param[in] dim_vec length of the vector
+ * @param[in] num_of_rows number of rows in weight matrix
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in] bias pointer to bias
+ * @param[in,out] pOut pointer to output vector
+ * @param[in,out] vec_buffer pointer to buffer space for input
+ * @return The function returns <code>ARM_MATH_SUCCESS</code>
+ *
+ */
+
+ arm_status arm_fully_connected_mat_q7_vec_q15_opt(const q15_t * pV,
+ const q7_t * pM,
+ const uint16_t dim_vec,
+ const uint16_t num_of_rows,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ const q7_t * bias,
+ q15_t * pOut,
+ q15_t * vec_buffer);
+
+/**
+ * @brief Matrix-Multiplication Kernels for Convolution
+ *
+ * These functions are used within convolution layer functions for
+ * matrix multiplication.
+ *
+ * The implementation is similar to CMSIS-DSP arm_mat_mult functions
+ * with one Q7 and one Q15 operands. The Q15 operand is the im2col
+ * output which is always with 2 columns.
+ *
+ */
+
+ /**
+ * @brief Matrix-multiplication function for convolution
+ * @param[in] pA pointer to operand A
+ * @param[in] pInBuffer pointer to operand B, always conssists of 2 vectors
+ * @param[in] ch_im_out numRow of A
+ * @param[in] numCol_A numCol of A
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in] bias the bias
+ * @param[in,out] pOut pointer to output
+ * @return The function returns the incremented output pointer
+ */
+
+ q7_t *arm_nn_mat_mult_kernel_q7_q15(const q7_t * pA,
+ const q15_t * pInBuffer,
+ const uint16_t ch_im_out,
+ const uint16_t numCol_A,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ const q7_t * bias,
+ q7_t * pOut);
+
+ /**
+ * @brief Matrix-multiplication function for convolution with reordered columns
+ * @param[in] pA pointer to operand A
+ * @param[in] pInBuffer pointer to operand B, always conssists of 2 vectors
+ * @param[in] ch_im_out numRow of A
+ * @param[in] numCol_A numCol of A
+ * @param[in] bias_shift amount of left-shift for bias
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in] bias the bias
+ * @param[in,out] pOut pointer to output
+ * @return The function returns the incremented output pointer
+ */
+
+ q7_t *arm_nn_mat_mult_kernel_q7_q15_reordered(const q7_t * pA,
+ const q15_t * pInBuffer,
+ const uint16_t ch_im_out,
+ const uint16_t numCol_A,
+ const uint16_t bias_shift,
+ const uint16_t out_shift,
+ const q7_t * bias,
+ q7_t * pOut);
+
+#ifdef __cplusplus
+}
+#endif
+
+/*
+ * Other functions
+ * These layers are typically not timing critical
+ * Basic implementation is supported here
+ */
+
+#ifdef __cplusplus
+extern "C"
+{
+#endif
+
+/**
+ * @defgroup Acti Neural Network Activation Functions
+ *
+ * Perform activation layers, including ReLU (Rectified Linear Unit),
+ * sigmoid and tanh
+ *
+ */
+
+ /**
+ * @brief Q7 RELU function
+ * @param[in,out] data pointer to input
+ * @param[in] size number of elements
+ * @return none.
+ */
+
+ void arm_relu_q7(q7_t * data, uint16_t size);
+
+ /**
+ * @brief Q15 RELU function
+ * @param[in,out] data pointer to input
+ * @param[in] size number of elements
+ * @return none.
+ */
+
+ void arm_relu_q15(q15_t * data, uint16_t size);
+
+ /**
+ * @brief Q7 neural network activation function using direct table look-up
+ * @param[in,out] data pointer to input
+ * @param[in] size number of elements
+ * @param[in] int_width bit-width of the integer part, assume to be smaller than 3
+ * @param[in] type type of activation functions
+ * @return none.
+ */
+
+ void arm_nn_activations_direct_q7(q7_t * data, uint16_t size, uint16_t int_width,
+ arm_nn_activation_type type);
+
+ /**
+ * @brief Q15 neural network activation function using direct table look-up
+ * @param[in,out] data pointer to input
+ * @param[in] size number of elements
+ * @param[in] int_width bit-width of the integer part, assume to be smaller than 3
+ * @param[in] type type of activation functions
+ * @return none.
+ */
+
+ void arm_nn_activations_direct_q15(q15_t * data, uint16_t size, uint16_t int_width,
+ arm_nn_activation_type type);
+
+/**
+ * @defgroup Pooling Neural Network Pooling Functions
+ *
+ * Perform pooling functions, including max pooling and average pooling
+ *
+ */
+
+ /**
+ * @brief Q7 max pooling function
+ * @param[in] Im_in pointer to input tensor
+ * @param[in] dim_im_in input tensor dimention
+ * @param[in] ch_im_in number of input tensor channels
+ * @param[in] dim_kernel filter kernel size
+ * @param[in] padding padding sizes
+ * @param[in] stride convolution stride
+ * @param[in] dim_im_out output tensor dimension
+ * @param[in,out] bufferA pointer to buffer space for input
+ * @param[in,out] Im_out pointer to output tensor
+ * @return none.
+ *
+ */
+
+ void arm_maxpool_q7_HWC(q7_t * Im_in,
+ const uint16_t dim_im_in,
+ const uint16_t ch_im_in,
+ const uint16_t dim_kernel,
+ const uint16_t padding,
+ const uint16_t stride,
+ const uint16_t dim_im_out,
+ q7_t * bufferA,
+ q7_t * Im_out);
+
+ /**
+ * @brief Q7 average pooling function
+ * @param[in] Im_in pointer to input tensor
+ * @param[in] dim_im_in input tensor dimention
+ * @param[in] ch_im_in number of input tensor channels
+ * @param[in] dim_kernel filter kernel size
+ * @param[in] padding padding sizes
+ * @param[in] stride convolution stride
+ * @param[in] dim_im_out output tensor dimension
+ * @param[in,out] bufferA pointer to buffer space for input
+ * @param[in,out] Im_out pointer to output tensor
+ * @return none.
+ *
+ */
+
+ void arm_avepool_q7_HWC(q7_t * Im_in,
+ const uint16_t dim_im_in,
+ const uint16_t ch_im_in,
+ const uint16_t dim_kernel,
+ const uint16_t padding,
+ const uint16_t stride,
+ const uint16_t dim_im_out,
+ q7_t * bufferA,
+ q7_t * Im_out);
+
+/**
+ * @defgroup Softmax Softmax Functions
+ *
+ * EXP(2) based softmax function
+ *
+ */
+
+ /**
+ * @brief Q7 softmax function
+ * @param[in] vec_in pointer to input vector
+ * @param[in] dim_vec input vector dimention
+ * @param[out] p_out pointer to output vector
+ * @return none.
+ *
+ */
+
+ void arm_softmax_q7(const q7_t * vec_in, const uint16_t dim_vec, q7_t * p_out);
+
+ /**
+ * @brief Q15 softmax function
+ * @param[in] vec_in pointer to input vector
+ * @param[in] dim_vec input vector dimention
+ * @param[out] p_out pointer to output vector
+ * @return none.
+ *
+ */
+
+ void arm_softmax_q15(const q15_t * vec_in, const uint16_t dim_vec, q15_t * p_out);
+
+ /**
+ * @brief uint8 depthwise convolution function with asymmetric quantization for even number of channel multiplier
+ * and input channels. Unless specified otherwise, arguments are mandatory.
+ *
+ * @param[in] input Pointer to input tensor
+ * @param[in] input_x Width of input tensor
+ * @param[in] input_y Height of input tensor
+ * @param[in] input_ch Channels in input tensor
+ * @param[in] kernel Pointer to kernel weights
+ * @param[in] kernel_x Width of kernel
+ * @param[in] kernel_y Height of kernel
+ * @param[in] ch_mult Number of channel multiplier
+ * @param[in] pad_x Padding sizes x
+ * @param[in] pad_y Padding sizes y
+ * @param[in] stride_x Convolution stride along the width
+ * @param[in] stride_y Convolution stride along the height
+ * @param[in] dilation_x Dilation along width. Not used and intended for future enhancement.
+ * @param[in] dilation_y Dilation along height. Not used and intended for future enhancement.
+ * @param[in] bias Pointer to optional bias values. If no bias is
+ * availble, NULL is expected
+ * @param[in] input_offset Input tensor zero offset
+ * @param[in] filter_offset Kernel tensor zero offset
+ * @param[in] output_offset Output tensor zero offset
+ * @param[in,out] output Pointer to output tensor
+ * @param[in] output_x Width of output tensor
+ * @param[in] output_y Height of output tensor
+ * @param[in] output_activation_min Minimum value to clamp the output to. Range : {0, 255}
+ * @param[in] output_activation_max Minimum value to clamp the output to. Range : {0, 255}
+ * @param[in] out_shift Amount of right-shift for output
+ * @param[in] out_mult Output multiplier for requantization
+ * @return The function returns one of the following
+ * <code>ARM_MATH_SIZE_MISMATCH</code> - Not supported dimension of tensors
+ * <code>ARM_MATH_SUCCESS</code> - Successful operation
+ * <code>ARM_MATH_ARGUMENT_ERROR</code> - Implementation not available
+ *
+ * <b> Input constraints</b>
+ * ch_mult is multiple of 2
+ * kernel_x is multiple of 2
+ *
+ */
+ arm_status arm_depthwise_conv_u8_basic_ver1(const uint8_t *input,
+ const uint16_t input_x,
+ const uint16_t input_y,
+ const uint16_t input_ch,
+ const uint8_t *kernel,
+ const uint16_t kernel_x,
+ const uint16_t kernel_y,
+ const int16_t ch_mult,
+ const int16_t pad_x,
+ const int16_t pad_y,
+ const int16_t stride_x,
+ const int16_t stride_y,
+ const int16_t dilation_x,
+ const int16_t dilation_y,
+ const int32_t *bias,
+ const int32_t input_offset,
+ const int32_t filter_offset,
+ const int32_t output_offset,
+ uint8_t *output,
+ const uint16_t output_x,
+ const uint16_t output_y,
+ const int32_t output_activation_min,
+ const int32_t output_activation_max,
+ const int32_t out_shift,
+ const int32_t out_mult);
+#ifdef __cplusplus
+}
+#endif
+
+#endif
diff --git a/Drivers/CMSIS/NN/Include/arm_nnsupportfunctions.h b/Drivers/CMSIS/NN/Include/arm_nnsupportfunctions.h index 4b50564..f9e06d0 100644 --- a/Drivers/CMSIS/NN/Include/arm_nnsupportfunctions.h +++ b/Drivers/CMSIS/NN/Include/arm_nnsupportfunctions.h @@ -1,1186 +1,269 @@ -/* - * Copyright (C) 2010-2022 Arm Limited or its affiliates. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_nnsupportfunctions.h - * Description: Public header file of support functions for CMSIS NN Library - * - * $Date: 19. April 2022 - * $Revision: V.7.0.1 - * - * Target Processor: Cortex-M CPUs - * -------------------------------------------------------------------- */ - -#ifndef _ARM_NNSUPPORTFUNCTIONS_H_ -#define _ARM_NNSUPPORTFUNCTIONS_H_ - -#include "arm_nn_math_types.h" -#include "arm_nn_types.h" - -#include <stdbool.h> - -#ifdef __cplusplus -extern "C" { -#endif - -#define LEFT_SHIFT(_shift) (_shift > 0 ? _shift : 0) -#define RIGHT_SHIFT(_shift) (_shift > 0 ? 0 : -_shift) -#define MASK_IF_ZERO(x) (x) == 0 ? ~0 : 0 -#define MASK_IF_NON_ZERO(x) (x) != 0 ? ~0 : 0 -#define SELECT_USING_MASK(mask, a, b) ((mask) & (a)) ^ (~(mask) & (b)) - -#define MAX(A, B) ((A) > (B) ? (A) : (B)) -#define MIN(A, B) ((A) < (B) ? (A) : (B)) -#define CLAMP(x, h, l) MAX(MIN((x), (h)), (l)) -#define REDUCE_MULTIPLIER(_mult) ((_mult < 0x7FFF0000) ? ((_mult + (1 << 15)) >> 16) : 0x7FFF) - -/** - * @brief definition to pack four 8 bit values. - */ -#define PACK_Q7x4_32x1(v0, v1, v2, v3) \ - ((((int32_t)(v0) << 0) & (int32_t)0x000000FF) | (((int32_t)(v1) << 8) & (int32_t)0x0000FF00) | \ - (((int32_t)(v2) << 16) & (int32_t)0x00FF0000) | (((int32_t)(v3) << 24) & (int32_t)0xFF000000)) - -/** - * @brief Union for SIMD access of q31/q15/q7 types - */ -union arm_nnword -{ - q31_t word; - /**< q31 type */ - q15_t half_words[2]; - /**< q15 type */ - q7_t bytes[4]; - /**< q7 type */ -}; - -/** - * @brief Union for data type long long - */ -struct arm_nn_double -{ - uint32_t low; - int32_t high; -}; - -union arm_nn_long_long -{ - int64_t long_long; - struct arm_nn_double word; -}; - -/** - * @defgroup nndata_convert Neural Network Data Conversion Functions - * - * Perform data type conversion in-between neural network operations - * - */ - -/** - * @brief Converts the elements of the q7 vector to q15 vector without left-shift - * @param[in] *pSrc points to the q7 input vector - * @param[out] *pDst points to the q15 output vector - * @param[in] blockSize length of the input vector - * - */ -void arm_q7_to_q15_no_shift(const q7_t *pSrc, q15_t *pDst, uint32_t blockSize); - -/** - * @brief Non-saturating addition of elements of a q7 vector - * @param[in] *input Pointer to the q7 input vector - * @param[out] *output Pointer to the q31 output variable. - * @param[in] block_size length of the input vector - * \par Description: - * - * 2^24 samples can be added without saturating the result. - * - * The equation used for the conversion process is: - * - * <pre> - * sum = input[0] + input[1] + .. + input[block_size -1] - * </pre> - * - * */ -void arm_nn_add_q7(const q7_t *input, q31_t *output, uint32_t block_size); - -/** - * @brief Converts the elements of the q7 vector to reordered q15 vector without left-shift - * @param[in] *pSrc points to the q7 input vector - * @param[out] *pDst points to the q15 output vector - * @param[in] blockSize length of the input vector - * @return none. - * - */ -void arm_q7_to_q15_reordered_no_shift(const q7_t *pSrc, q15_t *pDst, uint32_t blockSize); - -/** - * @brief Converts the elements from a q7 vector to a q15 vector with an added offset - * @param[in] src pointer to the q7 input vector - * @param[out] dst pointer to the q15 output vector - * @param[in] block_size length of the input vector - * @param[in] offset q7 offset to be added to each input vector element. - * - * \par Description: - * - * The equation used for the conversion process is: - * - * <pre> - * dst[n] = (q15_t) src[n] + offset; 0 <= n < block_size. - * </pre> - * - */ -void arm_q7_to_q15_with_offset(const q7_t *src, q15_t *dst, uint32_t block_size, q15_t offset); - -/** - * @brief Converts the elements of the q7 vector to reordered q15 vector with an added offset - * @param[in] src pointer to the q7 input vector - * @param[out] dst pointer to the q15 output vector - * @param[in] block_size length of the input vector - * @param[in] offset offset to be added to each input vector element. - * @return none. - * - * @details This function does the q7 to q15 expansion with re-ordering of bytes. Re-ordering is a consequence of - * the sign extension intrinsic(DSP extension). The tail (i.e., last (N % 4) elements) retains its - * original order. - * - */ -void arm_q7_to_q15_reordered_with_offset(const q7_t *src, q15_t *dst, uint32_t block_size, q15_t offset); - -/** - * @brief Converts the elements from a q7 vector and accumulate to a q15 vector - * @param[in] *src points to the q7 input vector - * @param[out] *dst points to the q15 output vector - * @param[in] block_size length of the input vector - * - * \par Description: - * - * The equation used for the conversion process is: - * - * <pre> - * dst[n] += (q15_t) src[n] ; 0 <= n < block_size. - * </pre> - * - */ -void arm_nn_accumulate_q7_to_q15(q15_t *dst, const q7_t *src, uint32_t block_size); - -/** - * @brief Depthwise conv on an im2col buffer where the input channel equals output channel. - * @param[in] row pointer to row - * @param[in] col pointer to im2col buffer, always consists of 2 columns. - * @param[in] num_ch number of channels - * @param[in] out_shift pointer to per output channel requantization shift parameter. - * @param[in] out_mult pointer to per output channel requantization multiplier parameter. - * @param[in] out_offset output tensor offset. - * @param[in] activation_min minimum value to clamp the output to. Range : int8 - * @param[in] activation_max maximum value to clamp the output to. Range : int8 - * @param[in] kernel_size number of elements in one column. - * @param[in] output_bias per output channel bias. Range : int32 - * @param[out] out pointer to output - * @return The function returns one of the two - * 1. The incremented output pointer for a successful operation or - * 2. NULL if implementation is not available. - * - * @details Supported framework: TensorFlow Lite micro. - */ -q7_t *arm_nn_depthwise_conv_s8_core(const q7_t *row, - const q15_t *col, - const uint16_t num_ch, - const int32_t *out_shift, - const int32_t *out_mult, - const int32_t out_offset, - const int32_t activation_min, - const int32_t activation_max, - const uint16_t kernel_size, - const int32_t *const output_bias, - q7_t *out); - -/** - * @brief General Matrix-multiplication function with per-channel requantization. - * @param[in] input_row pointer to row operand - * @param[in] input_col pointer to col operand - * @param[in] output_ch number of rows of input_row - * @param[in] col_batches number of column batches. Range: 1 to 4 - * @param[in] output_shift pointer to per output channel requantization shift parameter. - * @param[in] output_mult pointer to per output channel requantization multiplier parameter. - * @param[in] out_offset output tensor offset. - * @param[in] col_offset input tensor(col) offset. - * @param[in] row_offset kernel offset(row). Not used. - * @param[in] out_activation_min minimum value to clamp the output to. Range : int8 - * @param[in] out_activation_max maximum value to clamp the output to. Range : int8 - * @param[in] row_len number of elements in each row - * @param[in] bias per output channel bias. Range : int32 - * @param[in,out] out pointer to output - * @return The function returns one of the two - * 1. The incremented output pointer for a successful operation or - * 2. NULL if implementation is not available. - * - * @details Supported framework: TensorFlow Lite - */ -q7_t *arm_nn_mat_mult_s8(const q7_t *input_row, - const q7_t *input_col, - const uint16_t output_ch, - const uint16_t col_batches, - const int32_t *output_shift, - const int32_t *output_mult, - const int32_t out_offset, - const int32_t col_offset, - const int32_t row_offset, - const int16_t out_activation_min, - const int16_t out_activation_max, - const uint16_t row_len, - const int32_t *const bias, - q7_t *out); -/** - * @brief Matrix-multiplication function for convolution with per-channel requantization for 16 bits convolution. - * @param[in] input_a pointer to operand A - * @param[in] input_b pointer to operand B, always consists of 2 vectors. - * @param[in] output_ch number of rows of A - * @param[in] out_shift pointer to per output channel requantization shift parameter. - * @param[in] out_mult pointer to per output channel requantization multiplier parameter. - * @param[in] activation_min minimum value to clamp the output to. Range : int16 - * @param[in] activation_max maximum value to clamp the output to. Range : int16 - * @param[in] num_col_a number of columns of A - * @param[in] output_bias per output channel bias. Range : int64 - * @param[in,out] out_0 pointer to output - * @return The function returns one of the two - * 1. The incremented output pointer for a successful operation or - * 2. NULL if implementation is not available. - * - * @details This function does the matrix multiplication of weight matrix for all output channels - * with 2 columns from im2col and produces two elements/output_channel. The outputs are - * clamped in the range provided by activation min and max. - * Supported framework: TensorFlow Lite micro. - */ -q15_t *arm_nn_mat_mult_kernel_s16(const q7_t *input_a, - const q15_t *input_b, - const int32_t output_ch, - const int32_t *out_shift, - const int32_t *out_mult, - const int16_t activation_min, - const int16_t activation_max, - const int32_t num_col_a, - const int64_t *const output_bias, - q15_t *out_0); -/** - * @brief General Matrix-multiplication without requantization for one row & one column - * @param[in] row_elements number of row elements - * @param[in] row_base pointer to row operand - * @param[in] col_base pointer to col operand - * @param[out] sum_col pointer to store sum of column elements - * @param[out] output pointer to store result of multiply-accumulate - * @return The function returns the multiply-accumulated result of the row by column. - * - * @details Pseudo-code - * *output = 0 - * sum_col = 0 - * for (i = 0; i < row_elements; i++) - * *output += row_base[i] * col_base[i] - * sum_col += col_base[i] - * - */ -arm_status arm_nn_mat_mul_core_1x_s8(int32_t row_elements, - const int8_t *row_base, - const int8_t *col_base, - int32_t *const sum_col, - int32_t *const output); - -/** - * @brief Matrix-multiplication with requantization & activation function for four rows and one column - * @param[in] row_elements number of row elements - * @param[in] offset offset between rows. Can be the same as row_elements. - * For e.g, in a 1x1 conv scenario with stride as 1. - * @param[in] row_base pointer to row operand - * @param[in] col_base pointer to col operand - * @param[in] out_ch Number of output channels - * @param[in] conv_params Pointer to convolution parameters like offsets and activation values - * @param[in] quant_params Pointer to per-channel quantization parameters - * @param[in] bias Pointer to per-channel bias - * @param[out] output Pointer to output where int8 results are stored. - * - * @return The function returns the updated output pointer or NULL if implementation is not available. - * - * @details Compliant to TFLM int8 specification. MVE implementation only - */ -int8_t *arm_nn_mat_mul_core_4x_s8(const int32_t row_elements, - const int32_t offset, - const int8_t *row_base, - const int8_t *col_base, - const int32_t out_ch, - const cmsis_nn_conv_params *conv_params, - const cmsis_nn_per_channel_quant_params *quant_params, - const int32_t *bias, - int8_t *output); - -/** - * @brief General Matrix-multiplication function with per-channel requantization. - * This function assumes: - * - LHS input matrix NOT transposed (nt) - * - RHS input matrix transposed (t) - * - * @note This operation also performs the broadcast bias addition before the requantization - * - * @param[in] lhs Pointer to the LHS input matrix - * @param[in] rhs Pointer to the RHS input matrix - * @param[in] bias Pointer to the bias vector. The length of this vector is equal to the number of - * output columns (or RHS input rows) - * @param[out] dst Pointer to the output matrix with "m" rows and "n" columns - * @param[in] dst_multipliers Pointer to the multipliers vector needed for the per-channel requantization. - * The length of this vector is equal to the number of output columns (or RHS input - * rows) - * @param[in] dst_shifts Pointer to the shifts vector needed for the per-channel requantization. The length - * of this vector is equal to the number of output columns (or RHS input rows) - * @param[in] lhs_rows Number of LHS input rows - * @param[in] rhs_rows Number of RHS input rows - * @param[in] rhs_cols Number of LHS/RHS input columns - * @param[in] lhs_offset Offset to be applied to the LHS input value - * @param[in] dst_offset Offset to be applied the output result - * @param[in] activation_min Minimum value to clamp down the output. Range : int8 - * @param[in] activation_max Maximum value to clamp up the output. Range : int8 - * - * @return The function returns <code>ARM_MATH_SUCCESS</code> - * - */ -arm_status arm_nn_mat_mult_nt_t_s8(const q7_t *lhs, - const q7_t *rhs, - const q31_t *bias, - q7_t *dst, - const int32_t *dst_multipliers, - const int32_t *dst_shifts, - const int32_t lhs_rows, - const int32_t rhs_rows, - const int32_t rhs_cols, - const int32_t lhs_offset, - const int32_t dst_offset, - const int32_t activation_min, - const int32_t activation_max); - -/** - * @brief s8 Vector by Matrix (transposed) multiplication - * - * @param[in] lhs Input left-hand side vector - * @param[in] rhs Input right-hand side matrix (transposed) - * @param[in] bias Input bias - * @param[out] dst Output vector - * @param[in] lhs_offset Offset to be added to the input values of the left-hand side vector. - * Range: -127 to 128 - * @param[in] rhs_offset Not used - * @param[in] dst_offset Offset to be added to the output values. Range: -127 to 128 - * @param[in] dst_multiplier Output multiplier - * @param[in] dst_shift Output shift - * @param[in] rhs_cols Number of columns in the right-hand side input matrix - * @param[in] rhs_rows Number of rows in the right-hand side input matrix - * @param[in] activation_min Minimum value to clamp the output to. Range: int8 - * @param[in] activation_max Maximum value to clamp the output to. Range: int8 - * @param[in] address_offset Memory position offset for dst. First output is stored at 'dst', the - * second at 'dst + address_offset' and so on. Default value is typically 1. - * - * @return The function returns <code>ARM_MATH_SUCCESS</code> - * - */ -arm_status arm_nn_vec_mat_mult_t_s8(const q7_t *lhs, - const q7_t *rhs, - const q31_t *bias, - q7_t *dst, - const int32_t lhs_offset, - const int32_t rhs_offset, - const int32_t dst_offset, - const int32_t dst_multiplier, - const int32_t dst_shift, - const int32_t rhs_cols, - const int32_t rhs_rows, - const int32_t activation_min, - const int32_t activation_max, - const int32_t address_offset); - -/** - * @brief s16 Vector by Matrix (transposed) multiplication - * - * @param[in] lhs Input left-hand side vector - * @param[in] rhs Input right-hand side matrix (transposed) - * @param[in] bias Input bias - * @param[out] dst Output vector - * @param[in] dst_multiplier Output multiplier - * @param[in] dst_shift Output shift - * @param[in] rhs_cols Number of columns in the right-hand side input matrix - * @param[in] rhs_rows Number of rows in the right-hand side input matrix - * @param[in] activation_min Minimum value to clamp the output to. Range: int16 - * @param[in] activation_max Maximum value to clamp the output to. Range: int16 - * - * @return The function returns <code>ARM_MATH_SUCCESS</code> - * - */ -arm_status arm_nn_vec_mat_mult_t_s16(const q15_t *lhs, - const q7_t *rhs, - const q63_t *bias, - q15_t *dst, - const int32_t dst_multiplier, - const int32_t dst_shift, - const int32_t rhs_cols, - const int32_t rhs_rows, - const int32_t activation_min, - const int32_t activation_max); - -/** - * @brief s8 Vector by Matrix (transposed) multiplication with s16 output - * - * @param[in] lhs Input left-hand side vector - * @param[in] rhs Input right-hand side matrix (transposed) - * @param[out] dst Output vector - * @param[in] lhs_offset Offset to be added to the input values of the left-hand side - * vector. Range: -127 to 128 - * @param[in] rhs_offset Not used - * @param[in] scatter_offset Address offset for dst. First output is stored at 'dst', the - * second at 'dst + scatter_offset' and so on. - * @param[in] dst_multiplier Output multiplier - * @param[in] dst_shift Output shift - * @param[in] rhs_cols Number of columns in the right-hand side input matrix - * @param[in] rhs_rows Number of rows in the right-hand side input matrix - * @param[in] activation_min Minimum value to clamp the output to. Range: int16 - * @param[in] activation_max Maximum value to clamp the output to. Range: int16 - * - * @return The function returns <code>ARM_MATH_SUCCESS</code> - * - */ -arm_status arm_nn_vec_mat_mult_t_svdf_s8(const q7_t *lhs, - const q7_t *rhs, - q15_t *dst, - const int32_t lhs_offset, - const int32_t rhs_offset, - const int32_t scatter_offset, - const int32_t dst_multiplier, - const int32_t dst_shift, - const int32_t rhs_cols, - const int32_t rhs_rows, - const int32_t activation_min, - const int32_t activation_max); - -/** - * @brief Depthwise convolution of transposed rhs matrix with 4 lhs matrices. To be used in padded cases where - * the padding is -lhs_offset(Range: int8). Dimensions are the same for lhs and rhs. - * - * @param[in] lhs Input left-hand side matrix - * @param[in] rhs Input right-hand side matrix (transposed) - * @param[in] lhs_offset LHS matrix offset(input offset). Range: -127 to 128 - * @param[in] num_ch Number of channels in LHS/RHS - * @param[in] out_shift Per channel output shift. Length of vector is equal to number of channels - * @param[in] out_mult Per channel output multiplier. Length of vector is equal to number of channels - * @param[in] out_offset Offset to be added to the output values. Range: -127 to 128 - * @param[in] activation_min Minimum value to clamp the output to. Range: int8 - * @param[in] activation_max Maximum value to clamp the output to. Range: int8 - * @param[in] row_x_col (row_dimension * col_dimension) of LHS/RHS matrix - * @param[in] output_bias Per channel output bias. Length of vector is equal to number of channels - * @param[in] out Output pointer - * - * @return The function returns one of the two - * - Updated output pointer if an implementation is available - * - NULL if no implementation is available. - * - * @note If number of channels is not a multiple of 4, upto 3 elements outside the boundary will be read - * out for the following. - * - Output shift - * - Output multiplier - * - Output bias - * - rhs - */ -q7_t *arm_nn_depthwise_conv_nt_t_padded_s8(const q7_t *lhs, - const q7_t *rhs, - const int32_t lhs_offset, - const uint16_t num_ch, - const int32_t *out_shift, - const int32_t *out_mult, - const int32_t out_offset, - const int32_t activation_min, - const int32_t activation_max, - const uint16_t row_x_col, - const int32_t *const output_bias, - q7_t *out); - -/** - * @brief Depthwise convolution of transposed rhs matrix with 4 lhs matrices. To be used in non-padded cases. - * Dimensions are the same for lhs and rhs. - * - * @param[in] lhs Input left-hand side matrix - * @param[in] rhs Input right-hand side matrix (transposed) - * @param[in] lhs_offset LHS matrix offset(input offset). Range: -127 to 128 - * @param[in] num_ch Number of channels in LHS/RHS - * @param[in] out_shift Per channel output shift. Length of vector is equal to number of channels. - * @param[in] out_mult Per channel output multiplier. Length of vector is equal to number of channels. - * @param[in] out_offset Offset to be added to the output values. Range: -127 to 128 - * @param[in] activation_min Minimum value to clamp the output to. Range: int8 - * @param[in] activation_max Maximum value to clamp the output to. Range: int8 - * @param[in] row_x_col (row_dimension * col_dimension) of LHS/RHS matrix - * @param[in] output_bias Per channel output bias. Length of vector is equal to number of channels. - * @param[in] out Output pointer - * - * @return The function returns one of the two - * - Updated output pointer if an implementation is available - * - NULL if no implementation is available. - * - * @note If number of channels is not a multiple of 4, upto 3 elements outside the boundary will be read - * out for the following. - * - Output shift - * - Output multiplier - * - Output bias - * - rhs - */ -q7_t *arm_nn_depthwise_conv_nt_t_s8(const q7_t *lhs, - const q7_t *rhs, - const int32_t lhs_offset, - const uint16_t num_ch, - const int32_t *out_shift, - const int32_t *out_mult, - const int32_t out_offset, - const int32_t activation_min, - const int32_t activation_max, - const uint16_t row_x_col, - const int32_t *const output_bias, - q7_t *out); - -/** - *@brief Matrix-multiplication function for convolution with reordered columns - *@param[in] pA pointer to operand A - *@param[in] pInBuffer pointer to operand B, always conssists of 2 vectors - *@param[in] ch_im_out numRow of A - *@param[in] numCol_A numCol of A - *@param[in] bias_shift amount of left-shift for bias - *@param[in] out_shift amount of right-shift for output - *@param[in] bias the bias - *@param[in,out] pOut pointer to output - *@return The function returns the incremented output pointer - * - *@details This function assumes that data in pInBuffer are reordered - */ -q7_t *arm_nn_mat_mult_kernel_q7_q15_reordered(const q7_t *pA, - const q15_t *pInBuffer, - const uint16_t ch_im_out, - const uint16_t numCol_A, - const uint16_t bias_shift, - const uint16_t out_shift, - const q7_t *bias, - q7_t *pOut); - -/** - @brief Read 2 q15 elements and post increment pointer. - @param[in] in_q15 Pointer to pointer that holds address of input. - @return q31 value - */ -__STATIC_FORCEINLINE q31_t arm_nn_read_q15x2_ia(const q15_t **in_q15) -{ - q31_t val; - - memcpy(&val, *in_q15, 4); - *in_q15 += 2; - - return (val); -} - -/** - @brief Read 4 q7 from q7 pointer and post increment pointer. - @param[in] in_q7 Pointer to pointer that holds address of input. - @return q31 value - */ -__STATIC_FORCEINLINE q31_t arm_nn_read_q7x4_ia(const q7_t **in_q7) -{ - q31_t val; - memcpy(&val, *in_q7, 4); - *in_q7 += 4; - - return (val); -} - -/** - @brief Read 2 q15 from q15 pointer. - @param[in] in_q15 pointer to address of input. - @return q31 value - */ -__STATIC_FORCEINLINE q31_t arm_nn_read_q15x2(const q15_t *in_q15) -{ - q31_t val; - memcpy(&val, in_q15, 4); - - return (val); -} - -/** - @brief Read 4 q7 values. - @param[in] in_q7 pointer to address of input. - @return q31 value - */ -__STATIC_FORCEINLINE q31_t arm_nn_read_q7x4(const q7_t *in_q7) -{ - q31_t val; - memcpy(&val, in_q7, 4); - - return (val); -} - -/** - @brief Write four q7 to q7 pointer and increment pointer afterwards. - @param[in] in Double pointer to input value - @param[in] value Four bytes to copy - */ -__STATIC_FORCEINLINE void arm_nn_write_q7x4_ia(q7_t **in, q31_t value) -{ - memcpy(*in, &value, 4); - *in += 4; -} - -/** - * @brief memset optimized for MVE - * @param[in, out] dst Destination pointer - * @param[in] val Value to set - * @param[in] block_size Number of bytes to copy. - * - */ -__STATIC_FORCEINLINE void arm_memset_q7(q7_t *dst, const q7_t val, uint32_t block_size) -{ -#if defined(ARM_MATH_MVEI) - __asm volatile(" vdup.8 q0, %[set_val] \n" - " wlstp.8 lr, %[cnt], 1f \n" - "2: \n" - " vstrb.8 q0, [%[in]], #16 \n" - " letp lr, 2b \n" - "1: \n" - : [ in ] "+r"(dst) - : [ cnt ] "r"(block_size), [ set_val ] "r"(val) - : "q0", "memory", "r14"); -#else - memset(dst, val, block_size); -#endif -} - -#if defined(ARM_MATH_DSP) - -/** - * @brief read and expand one q7 word into two q15 words - */ - -__STATIC_FORCEINLINE const q7_t *read_and_pad(const q7_t *source, q31_t *out1, q31_t *out2) -{ - q31_t inA = arm_nn_read_q7x4_ia(&source); - q31_t inAbuf1 = __SXTB16_RORn((uint32_t)inA, 8); - q31_t inAbuf2 = __SXTB16(inA); - -#ifndef ARM_MATH_BIG_ENDIAN - *out2 = (int32_t)(__PKHTB(inAbuf1, inAbuf2, 16)); - *out1 = (int32_t)(__PKHBT(inAbuf2, inAbuf1, 16)); -#else - *out1 = (int32_t)(__PKHTB(inAbuf1, inAbuf2, 16)); - *out2 = (int32_t)(__PKHBT(inAbuf2, inAbuf1, 16)); -#endif - - return source; -} - -/** - * @brief read and expand one q7 word into two q15 words with reordering - */ - -__STATIC_FORCEINLINE const q7_t *read_and_pad_reordered(const q7_t *source, q31_t *out1, q31_t *out2) -{ - q31_t inA = arm_nn_read_q7x4_ia(&source); -#ifndef ARM_MATH_BIG_ENDIAN - *out2 = __SXTB16(__ROR((uint32_t)inA, 8)); - *out1 = __SXTB16(inA); -#else - *out1 = __SXTB16(__ROR((uint32_t)inA, 8)); - *out2 = __SXTB16(inA); -#endif - - return source; -} - -/** - * @brief read and expand one q7 word into two q15 words with reordering and add an offset - */ -__STATIC_FORCEINLINE const q7_t * -read_and_pad_reordered_with_offset(const q7_t *source, q31_t *out1, q31_t *out2, q31_t offset) -{ - q31_t inA = arm_nn_read_q7x4_ia(&source); - -#ifndef ARM_MATH_BIG_ENDIAN - *out2 = __SXTB16(__ROR((uint32_t)inA, 8)); - *out1 = __SXTB16(inA); -#else - *out1 = __SXTB16(__ROR((uint32_t)inA, 8)); - *out2 = __SXTB16(inA); -#endif - *out1 = __QADD16(*out1, offset); - *out2 = __QADD16(*out2, offset); - - return source; -} - -#endif - -/** - * @defgroup NNBasicMath Basic Math Functions for Neural Network Computation - * - * Basic Math Functions for Neural Network Computation - * - */ - -/** - * @brief q7 vector multiplication with variable output shifts - * @param[in] *pSrcA pointer to the first input vector - * @param[in] *pSrcB pointer to the second input vector - * @param[out] *pDst pointer to the output vector - * @param[in] out_shift amount of right-shift for output - * @param[in] blockSize number of samples in each vector - * @return none. - * - * <b>Scaling and Overflow Behavior:</b> - * \par - * The function uses saturating arithmetic. - * Results outside of the allowable q15 range [0x8000 0x7FFF] will be saturated. - */ - -void arm_nn_mult_q15(q15_t *pSrcA, q15_t *pSrcB, q15_t *pDst, const uint16_t out_shift, uint32_t blockSize); - -/** - * @brief q7 vector multiplication with variable output shifts - * @param[in] *pSrcA pointer to the first input vector - * @param[in] *pSrcB pointer to the second input vector - * @param[out] *pDst pointer to the output vector - * @param[in] out_shift amount of right-shift for output - * @param[in] blockSize number of samples in each vector - * @return none. - * - * <b>Scaling and Overflow Behavior:</b> - * \par - * The function uses saturating arithmetic. - * Results outside of the allowable q7 range [0x80 0x7F] will be saturated. - */ - -void arm_nn_mult_q7(q7_t *pSrcA, q7_t *pSrcB, q7_t *pDst, const uint16_t out_shift, uint32_t blockSize); - -/** - * @brief Matrix-multiplication function for convolution with per-channel requantization. - * @param[in] input_a pointer to operand A - * @param[in] input_b pointer to operand B, always consists of 2 vectors. - * @param[in] output_ch number of rows of A - * @param[in] out_shift pointer to per output channel requantization shift parameter. - * @param[in] out_mult pointer to per output channel requantization multiplier parameter. - * @param[in] out_offset output tensor offset. - * @param[in] activation_min minimum value to clamp the output to. Range : int8 - * @param[in] activation_max maximum value to clamp the output to. Range : int8 - * @param[in] num_col_a number of columns of A - * @param[in] output_bias per output channel bias. Range : int32 - * @param[in,out] out_0 pointer to output - * @return The function returns one of the two - * 1. The incremented output pointer for a successful operation or - * 2. NULL if implementation is not available. - * - * @details This function does the matrix multiplication of weight matrix for all output channels - * with 2 columns from im2col and produces two elements/output_channel. The outputs are - * clamped in the range provided by activation min and max. - * Supported framework: TensorFlow Lite micro. - */ -q7_t *arm_nn_mat_mult_kernel_s8_s16(const q7_t *input_a, - const q15_t *input_b, - const uint16_t output_ch, - const int32_t *out_shift, - const int32_t *out_mult, - const int32_t out_offset, - const int16_t activation_min, - const int16_t activation_max, - const uint16_t num_col_a, - const int32_t *const output_bias, - q7_t *out_0); - -/** - * @brief Common softmax function for s8 input and s8 or s16 output - * @param[in] input Pointer to the input tensor - * @param[in] num_rows Number of rows in the input tensor - * @param[in] row_size Number of elements in each input row - * @param[in] mult Input quantization multiplier - * @param[in] shift Input quantization shift within the range [0, 31] - * @param[in] diff_min Minimum difference with max in row. Used to check if - * the quantized exponential operation can be performed - * @param[in] int16_output Indicating s8 output if 0 else s16 output - * @param[out] output Pointer to the output tensor - * - * @note Supported framework: TensorFlow Lite micro (bit-accurate) - * - */ -void arm_nn_softmax_common_s8(const int8_t *input, - const int32_t num_rows, - const int32_t row_size, - const int32_t mult, - const int32_t shift, - const int32_t diff_min, - const bool int16_output, - void *output); - -/** - * @brief macro for adding rounding offset - */ -#ifndef ARM_NN_TRUNCATE -#define NN_ROUND(out_shift) ((0x1 << out_shift) >> 1) -#else -#define NN_ROUND(out_shift) 0 -#endif - -// Macros for shortening quantization functions' names and avoid long lines -#define MUL_SAT(a, b) arm_nn_doubling_high_mult((a), (b)) -#define MUL_SAT_MVE(a, b) arm_doubling_high_mult_mve_32x4((a), (b)) -#define MUL_POW2(a, b) arm_nn_mult_by_power_of_two((a), (b)) - -#define DIV_POW2(a, b) arm_nn_divide_by_power_of_two((a), (b)) -#define DIV_POW2_MVE(a, b) arm_divide_by_power_of_two_mve((a), (b)) - -#define EXP_ON_NEG(x) arm_nn_exp_on_negative_values((x)) -#define ONE_OVER1(x) arm_nn_one_over_one_plus_x_for_x_in_0_1((x)) - -/** - * @brief Saturating doubling high multiply. Result matches - * NEON instruction VQRDMULH. - * @param[in] m1 Multiplicand. Range: {NN_Q31_MIN, NN_Q31_MAX} - * @param[in] m2 Multiplier. Range: {NN_Q31_MIN, NN_Q31_MAX} - * @return Result of multiplication. - * - */ -__STATIC_FORCEINLINE q31_t arm_nn_doubling_high_mult(const q31_t m1, const q31_t m2) -{ - q31_t result = 0; - // Rounding offset to add for a right shift of 31 - q63_t mult = 1 << 30; - - if ((m1 < 0) ^ (m2 < 0)) - { - mult = 1 - mult; - } - // Gets resolved as a SMLAL instruction - mult = mult + (q63_t)m1 * m2; - - // Utilize all of the upper 32 bits. This is the doubling step - // as well. - result = (int32_t)(mult / (1ll << 31)); - - if ((m1 == m2) && (m1 == (int32_t)NN_Q31_MIN)) - { - result = NN_Q31_MAX; - } - return result; -} - -/** - * @brief Doubling high multiply without saturation. This is intended - * for requantization where the scale is a positive integer - * - * @param[in] m1 Multiplicand. Range: {NN_Q31_MIN, NN_Q31_MAX} - * @param[in] m2 Multiplier Range: {NN_Q31_MIN, NN_Q31_MAX} - * @return Result of multiplication. - * @note The result of this matches that of neon instruction - * VQRDMULH for m1 in range {NN_Q31_MIN, NN_Q31_MAX} and m2 in - * range {NN_Q31_MIN + 1, NN_Q31_MAX}. Saturation occurs when - * m1 equals m2 equals NN_Q31_MIN and that is not handled by - * this function. - * - */ -__STATIC_FORCEINLINE q31_t arm_nn_doubling_high_mult_no_sat(const q31_t m1, const q31_t m2) -{ - q31_t result = 0; - union arm_nn_long_long mult; - - // Rounding offset to add for a right shift of 31 - mult.word.low = 1 << 30; - mult.word.high = 0; - - // Gets resolved as a SMLAL instruction - mult.long_long = mult.long_long + (q63_t)m1 * m2; - - // Utilize all of the upper 32 bits. This is the doubling step - // as well. - result = (int32_t)(mult.long_long >> 31); - - return result; -} - -/** - * @brief Rounding divide by power of two. - * @param[in] dividend - Dividend - * @param[in] exponent - Divisor = power(2, exponent) - * Range: [0, 31] - * @return Rounded result of division. Midpoint is rounded away from zero. - * - */ -__STATIC_FORCEINLINE q31_t arm_nn_divide_by_power_of_two(const q31_t dividend, const q31_t exponent) -{ - q31_t result = 0; - const q31_t remainder_mask = (1 << exponent) - 1; - int32_t remainder = remainder_mask & dividend; - - // Basic division - result = dividend >> exponent; - - // Adjust 'result' for rounding (mid point away from zero) - q31_t threshold = remainder_mask >> 1; - if (result < 0) - { - threshold++; - } - if (remainder > threshold) - { - result++; - } - - return result; -} - -/** - * @brief Requantize a given value. - * @param[in] val Value to be requantized - * @param[in] multiplier multiplier. Range {NN_Q31_MIN + 1, Q32_MAX} - * @param[in] shift left or right shift for 'val * multiplier' - * - * @return Returns (val * multiplier)/(2 ^ shift) - * - */ -__STATIC_FORCEINLINE q31_t arm_nn_requantize(const q31_t val, const q31_t multiplier, const q31_t shift) -{ -#ifdef CMSIS_NN_USE_SINGLE_ROUNDING - const int64_t total_shift = 31 - shift; - const int64_t new_val = val * (int64_t)multiplier; - - int32_t result = new_val >> (total_shift - 1); - result = (result + 1) >> 1; - - return result; -#else - return arm_nn_divide_by_power_of_two(arm_nn_doubling_high_mult_no_sat(val * (1 << LEFT_SHIFT(shift)), multiplier), - RIGHT_SHIFT(shift)); -#endif -} - -/** - * @brief Requantize a given 64 bit value. - * @param[in] val Value to be requantized in the range {-(1<<47)} to {(1<<47) - 1} - * @param[in] reduced_multiplier Reduced multiplier in the range {NN_Q31_MIN + 1, Q32_MAX} to {Q16_MIN + 1, - * Q16_MAX} - * @param[in] shift Left or right shift for 'val * multiplier' in the range {-31} to {7} - * - * @return Returns (val * multiplier)/(2 ^ shift) - * - */ -__STATIC_FORCEINLINE q31_t arm_nn_requantize_s64(const q63_t val, const q31_t reduced_multiplier, const q31_t shift) -{ - const q63_t new_val = val * reduced_multiplier; - - q31_t result = new_val >> (14 - shift); // 64->32 bit reduction - result = (result + 1) >> 1; // Last shift position and insert round - - return result; -} - -/** - * @brief memcpy optimized for MVE - * @param[in, out] dst Destination pointer - * @param[in] src Source pointer. - * @param[in] block_size Number of bytes to copy. - * - */ -__STATIC_FORCEINLINE void arm_memcpy_q7(q7_t *__RESTRICT dst, const q7_t *__RESTRICT src, uint32_t block_size) -{ -#if defined(ARM_MATH_MVEI) - __asm volatile(" wlstp.8 lr, %[cnt], 1f \n" - "2: \n" - " vldrb.8 q0, [%[in]], #16 \n" - " vstrb.8 q0, [%[out]], #16 \n" - " letp lr, 2b \n" - "1: \n" - : [ in ] "+r"(src), [ out ] "+r"(dst) - : [ cnt ] "r"(block_size) - : "q0", "memory", "r14"); -#else - memcpy(dst, src, block_size); -#endif -} - -#if defined(ARM_MATH_MVEI) -/** - * @brief Vector saturating doubling high multiply returning high half. - * @param[in] m1 Multiplicand - * @param[in] m2 Multiplier - * @return Result of multiplication. - * - */ -__STATIC_FORCEINLINE int32x4_t arm_doubling_high_mult_mve(const int32x4_t m1, const q31_t m2) -{ - return vqrdmulhq_n_s32(m1, m2); -} - -/** - * @brief Vector rounding divide by power of two. - * @param[in] dividend - Dividend vector - * @param[in] exponent - Divisor = power(2, exponent) - * Range: [0, 31] - * @return Rounded result of division. Midpoint is rounded away from zero. - * - */ -__STATIC_FORCEINLINE int32x4_t arm_divide_by_power_of_two_mve(const int32x4_t dividend, const q31_t exponent) -{ - const int32x4_t shift = vdupq_n_s32(-exponent); - const int32x4_t fixup = vshrq_n_s32(vandq_s32(dividend, shift), 31); - const int32x4_t fixed_up_dividend = vqaddq_s32(dividend, fixup); - return vrshlq_s32(fixed_up_dividend, shift); -} - -/** - * @brief Requantize a given vector. - * @param[in] val Vector to be requantized - * @param[in] multiplier multiplier - * @param[in] shift shift - * - * @return Returns (val * multiplier)/(2 ^ shift) - * - */ -__STATIC_FORCEINLINE int32x4_t arm_requantize_mve(const int32x4_t val, const q31_t multiplier, const q31_t shift) -{ -#ifdef CMSIS_NN_USE_SINGLE_ROUNDING - const int right_shift = MIN(-1, shift); - const int left_shift = shift - right_shift; - - const int32x4_t left_shift_dup = vdupq_n_s32(left_shift); - const int32x4_t right_shift_dup = vdupq_n_s32(right_shift); - - int32x4_t result = vqdmulhq_n_s32(vshlq_s32(val, left_shift_dup), multiplier); - result = vrshlq_s32(result, right_shift_dup); - - return result; -#else - return arm_divide_by_power_of_two_mve( - arm_doubling_high_mult_mve(vshlq_s32(val, vdupq_n_s32(LEFT_SHIFT(shift))), multiplier), RIGHT_SHIFT(shift)); -#endif -} - -__STATIC_FORCEINLINE int32x4_t arm_doubling_high_mult_mve_32x4(const int32x4_t m1, const int32x4_t m2) -{ - return vqrdmulhq_s32(m1, m2); -} - -__STATIC_FORCEINLINE int32x4_t arm_divide_by_power_of_two_mve_32x4(const int32x4_t dividend, const int32x4_t exponent) -{ - const int32x4_t shift = -exponent; - const int32x4_t fixup = vshrq_n_s32(vandq_s32(dividend, shift), 31); - const int32x4_t fixed_up_dividend = vqaddq_s32(dividend, fixup); - return vrshlq_s32(fixed_up_dividend, shift); -} - -__STATIC_FORCEINLINE int32x4_t arm_requantize_mve_32x4(const int32x4_t val, - const int32x4_t multiplier, - const int32x4_t shift) -{ -#ifdef CMSIS_NN_USE_SINGLE_ROUNDING - const int32x4_t right_shift = vminq_s32(vdupq_n_s32(-1), shift); - const int32x4_t left_shift = vqsubq_s32(shift, right_shift); - - int32x4_t result = vqdmulhq_s32(vshlq_s32(val, left_shift), multiplier); - result = vrshlq_s32(result, right_shift); - - return result; -#else - const int32x4_t zz = vdupq_n_s32(0); - const mve_pred16_t p = vcmpgtq_n_s32(shift, 0); - - const int32x4_t left_shift = vpselq_s32(shift, zz, p); - const int32x4_t right_shift = -vpselq_s32(zz, shift, p); - - return arm_divide_by_power_of_two_mve_32x4(arm_doubling_high_mult_mve_32x4(vshlq_s32(val, left_shift), multiplier), - right_shift); -#endif -} -#endif - -// @note The following functions are used only for softmax layer, scaled bits = 5 assumed - -__STATIC_FORCEINLINE int32_t arm_nn_exp_on_negative_values(int32_t val) -{ - int32_t mask = 0; - int32_t shift = 24; - - const int32_t val_mod_minus_quarter = (val & ((1 << shift) - 1)) - (1 << shift); - const int32_t remainder = val_mod_minus_quarter - val; - const int32_t x = (val_mod_minus_quarter << 5) + (1 << 28); - const int32_t x2 = MUL_SAT(x, x); - - int32_t result = 1895147668 + - MUL_SAT(1895147668, x + DIV_POW2(MUL_SAT(DIV_POW2(MUL_SAT(x2, x2), 2) + MUL_SAT(x2, x), 715827883) + x2, 1)); - -#define SELECT_IF_NON_ZERO(x) \ - { \ - mask = MASK_IF_NON_ZERO(remainder & (1 << shift++)); \ - result = SELECT_USING_MASK(mask, MUL_SAT(result, x), result); \ - } - - SELECT_IF_NON_ZERO(1672461947) - SELECT_IF_NON_ZERO(1302514674) - SELECT_IF_NON_ZERO(790015084) - SELECT_IF_NON_ZERO(290630308) - SELECT_IF_NON_ZERO(39332535) - SELECT_IF_NON_ZERO(720401) - SELECT_IF_NON_ZERO(242) - -#undef SELECT_IF_NON_ZERO - - mask = MASK_IF_ZERO(val); - return SELECT_USING_MASK(mask, NN_Q31_MAX, result); -} - -__STATIC_FORCEINLINE q31_t arm_nn_mult_by_power_of_two(const int32_t val, const int32_t exp) -{ - const int32_t thresh = ((1 << (31 - exp)) - 1); - int32_t result = val << exp; - result = SELECT_USING_MASK(MASK_IF_NON_ZERO(val > thresh), NN_Q31_MAX, result); - result = SELECT_USING_MASK(MASK_IF_NON_ZERO(val < -thresh), NN_Q31_MIN, result); - return result; -} - -__STATIC_FORCEINLINE int32_t arm_nn_one_over_one_plus_x_for_x_in_0_1(int32_t val) -{ - const int64_t sum = (int64_t)val + (int64_t)NN_Q31_MAX; - const int32_t half_denominator = (int32_t)((sum + (sum >= 0 ? 1 : -1)) / 2L); - int32_t x = 1515870810 + MUL_SAT(half_denominator, -1010580540); - - const int32_t shift = (1 << 29); - x += MUL_POW2(MUL_SAT(x, shift - MUL_SAT(half_denominator, x)), 2); - x += MUL_POW2(MUL_SAT(x, shift - MUL_SAT(half_denominator, x)), 2); - x += MUL_POW2(MUL_SAT(x, shift - MUL_SAT(half_denominator, x)), 2); - - return MUL_POW2(x, 1); -} - -/** - @brief Write 2 q15 elements and post increment pointer. - @param[in] dest_q15 Pointer to pointer that holds address of destination. - @param[in] src_q31 Input value to be written. - */ -__STATIC_FORCEINLINE void arm_nn_write_q15x2_ia(q15_t **dest_q15, q31_t src_q31) -{ - q31_t val = src_q31; - - memcpy(*dest_q15, &val, 4); - *dest_q15 += 2; -} - -#ifdef __cplusplus -} -#endif - -#endif +/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project: CMSIS NN Library
+ * Title: arm_nnsupportfunctions.h
+ * Description: Public header file of support functions for CMSIS NN Library
+ *
+ * $Date: 13. July 2018
+ * $Revision: V.1.0.0
+ *
+ * Target Processor: Cortex-M cores
+ * -------------------------------------------------------------------- */
+
+#ifndef _ARM_NNSUPPORTFUNCTIONS_H_
+#define _ARM_NNSUPPORTFUNCTIONS_H_
+
+#include "arm_math.h"
+#include "arm_common_tables.h"
+
+#ifdef __cplusplus
+extern "C"
+{
+#endif
+
+#define LEFT_SHIFT(_shift) (_shift > 0 ? _shift : 0)
+#define RIGHT_SHIFT(_shift) (_shift > 0 ? 0 : -_shift)
+#define Q31_MIN (0x80000000L)
+#define Q31_MAX (0x7FFFFFFFL)
+
+/**
+ * @brief Union for SIMD access of Q31/Q15/Q7 types
+ */
+union arm_nnword
+{
+ q31_t word;
+ /**< Q31 type */
+ q15_t half_words[2];
+ /**< Q15 type */
+ q7_t bytes[4];
+ /**< Q7 type */
+};
+
+/**
+ * @brief Struct for specifying activation function types
+ *
+ */
+typedef enum
+{
+ ARM_SIGMOID = 0,
+ /**< Sigmoid activation function */
+ ARM_TANH = 1,
+ /**< Tanh activation function */
+} arm_nn_activation_type;
+
+/**
+ * @defgroup nndata_convert Neural Network Data Conversion Functions
+ *
+ * Perform data type conversion in-between neural network operations
+ *
+ */
+
+/**
+ * @brief Converts the elements of the Q7 vector to Q15 vector without left-shift
+ * @param[in] *pSrc points to the Q7 input vector
+ * @param[out] *pDst points to the Q15 output vector
+ * @param[in] blockSize length of the input vector
+ * @return none.
+ *
+ */
+
+void arm_q7_to_q15_no_shift(const q7_t * pSrc, q15_t * pDst, uint32_t blockSize);
+
+/**
+ * @brief Converts the elements of the Q7 vector to reordered Q15 vector without left-shift
+ * @param[in] *pSrc points to the Q7 input vector
+ * @param[out] *pDst points to the Q15 output vector
+ * @param[in] blockSize length of the input vector
+ * @return none.
+ *
+ */
+
+void arm_q7_to_q15_reordered_no_shift(const q7_t * pSrc, q15_t * pDst, uint32_t blockSize);
+
+#if defined (ARM_MATH_DSP)
+
+/**
+ * @brief read and expand one Q7 word into two Q15 words
+ */
+
+__STATIC_FORCEINLINE void *read_and_pad(void *source, q31_t * out1, q31_t * out2)
+{
+ q31_t inA = *__SIMD32(source)++;
+ q31_t inAbuf1 = __SXTB16(__ROR(inA, 8));
+ q31_t inAbuf2 = __SXTB16(inA);
+
+#ifndef ARM_MATH_BIG_ENDIAN
+ *out2 = __PKHTB(inAbuf1, inAbuf2, 16);
+ *out1 = __PKHBT(inAbuf2, inAbuf1, 16);
+#else
+ *out1 = __PKHTB(inAbuf1, inAbuf2, 16);
+ *out2 = __PKHBT(inAbuf2, inAbuf1, 16);
+#endif
+
+ return source;
+}
+
+/**
+ * @brief read and expand one Q7 word into two Q15 words with reordering
+ */
+
+__STATIC_FORCEINLINE void *read_and_pad_reordered(void *source, q31_t * out1, q31_t * out2)
+{
+ q31_t inA = *__SIMD32(source)++;
+#ifndef ARM_MATH_BIG_ENDIAN
+ *out2 = __SXTB16(__ROR(inA, 8));
+ *out1 = __SXTB16(inA);
+#else
+ *out1 = __SXTB16(__ROR(inA, 8));
+ *out2 = __SXTB16(inA);
+#endif
+
+ return source;
+}
+#endif
+
+/**
+ * @defgroup NNBasicMath Basic Math Functions for Neural Network Computation
+ *
+ * Basic Math Functions for Neural Network Computation
+ *
+ */
+
+/**
+ * @brief Q7 vector multiplication with variable output shifts
+ * @param[in] *pSrcA pointer to the first input vector
+ * @param[in] *pSrcB pointer to the second input vector
+ * @param[out] *pDst pointer to the output vector
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in] blockSize number of samples in each vector
+ * @return none.
+ *
+ * <b>Scaling and Overflow Behavior:</b>
+ * \par
+ * The function uses saturating arithmetic.
+ * Results outside of the allowable Q15 range [0x8000 0x7FFF] will be saturated.
+ */
+
+void arm_nn_mult_q15(
+ q15_t * pSrcA,
+ q15_t * pSrcB,
+ q15_t * pDst,
+ const uint16_t out_shift,
+ uint32_t blockSize);
+
+/**
+ * @brief Q7 vector multiplication with variable output shifts
+ * @param[in] *pSrcA pointer to the first input vector
+ * @param[in] *pSrcB pointer to the second input vector
+ * @param[out] *pDst pointer to the output vector
+ * @param[in] out_shift amount of right-shift for output
+ * @param[in] blockSize number of samples in each vector
+ * @return none.
+ *
+ * <b>Scaling and Overflow Behavior:</b>
+ * \par
+ * The function uses saturating arithmetic.
+ * Results outside of the allowable Q7 range [0x80 0x7F] will be saturated.
+ */
+
+void arm_nn_mult_q7(
+ q7_t * pSrcA,
+ q7_t * pSrcB,
+ q7_t * pDst,
+ const uint16_t out_shift,
+ uint32_t blockSize);
+
+/**
+ * @brief macro for adding rounding offset
+ */
+#ifndef ARM_NN_TRUNCATE
+ #define NN_ROUND(out_shift) ( (0x1u << out_shift) >> 1 )
+#else
+ #define NN_ROUND(out_shift) 0
+#endif
+
+/**
+ * @brief Saturating doubling high multiply. Result matches
+ * NEON instruction VQRDMULH.
+ * @param[in] m1 Multiplicand
+ * @param[in] m2 Multiplier
+ * @return Result of multiplication.
+ *
+ */
+__STATIC_FORCEINLINE q31_t arm_nn_sat_doubling_high_mult(const q31_t m1, const q31_t m2)
+{
+ q31_t result = 0;
+ // Rounding offset to add for a right shift of 31
+ q63_t mult = 1 << 30;
+
+ if ((m1 < 0) ^ (m2 < 0))
+ {
+ mult = 1 - mult;
+ }
+ // Gets resolved as a SMLAL instruction
+ mult = mult + (q63_t)m1 * m2;
+
+ // Utilize all of the upper 32 bits. This is the doubling step
+ // as well.
+ result = mult / (1UL << 31);
+
+ if ((m1 == m2) && (m1 == Q31_MIN))
+ {
+ result = Q31_MAX;
+ }
+ return result;
+}
+
+/**
+ * @brief Rounding divide by power of two.
+ * @param[in] dividend - Dividend
+ * @param[in] exponent - Divisor = power(2, exponent)
+ * Range: [0, 31]
+ * @return Rounded result of division. Midpoint is rounded away from zero.
+ *
+ */
+__STATIC_FORCEINLINE q31_t arm_nn_divide_by_power_of_two(const q31_t dividend, const q31_t exponent)
+{
+ q31_t result = 0;
+ const q31_t remainder_mask = (1l << exponent) - 1;
+ int32_t remainder = remainder_mask & dividend;
+
+ // Basic division
+ result = dividend >> exponent;
+
+ // Adjust 'result' for rounding (mid point away from zero)
+ q31_t threshold = remainder_mask >> 1;
+ if (result < 0)
+ {
+ threshold++;
+ }
+ if (remainder > threshold)
+ {
+ result++;
+ }
+
+ return result;
+}
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif
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