diff options
| author | Clyne Sullivan <clyne@bitgloo.com> | 2025-01-29 21:34:25 -0500 |
|---|---|---|
| committer | Clyne Sullivan <clyne@bitgloo.com> | 2025-01-29 21:34:25 -0500 |
| commit | 5b81bc8ccbd342b8566d88fc9f17a73aec03b5b6 (patch) | |
| tree | cc57486912cfa74c6440d8b97c28f451ec787d78 /Drivers/CMSIS/NN/Include | |
initial commit
Diffstat (limited to 'Drivers/CMSIS/NN/Include')
| -rw-r--r-- | Drivers/CMSIS/NN/Include/arm_nn_math_types.h | 169 | ||||
| -rw-r--r-- | Drivers/CMSIS/NN/Include/arm_nn_tables.h | 56 | ||||
| -rw-r--r-- | Drivers/CMSIS/NN/Include/arm_nn_types.h | 137 | ||||
| -rw-r--r-- | Drivers/CMSIS/NN/Include/arm_nnfunctions.h | 2532 | ||||
| -rw-r--r-- | Drivers/CMSIS/NN/Include/arm_nnsupportfunctions.h | 1186 |
5 files changed, 4080 insertions, 0 deletions
diff --git a/Drivers/CMSIS/NN/Include/arm_nn_math_types.h b/Drivers/CMSIS/NN/Include/arm_nn_math_types.h new file mode 100644 index 0000000..390fe78 --- /dev/null +++ b/Drivers/CMSIS/NN/Include/arm_nn_math_types.h @@ -0,0 +1,169 @@ +/****************************************************************************** + * @file arm_nn_math_types.h + * @brief Compiler include and basic types + * @version V1.1.0 + * @date 09 March 2022 + * Target Processor: Cortex-M + ******************************************************************************/ +/* + * 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. + */ + +/** + Copied from CMSIS/DSP/arm_math_types.h and modified +*/ + +#ifndef _ARM_NN_MATH_TYPES_H_ + +#define _ARM_NN_MATH_TYPES_H_ + +/* DSP inlcude for enum arm_status. */ +#include "arm_math_types.h" + +#ifdef __cplusplus +extern "C" { +#endif + +/* Compiler specific diagnostic adjustment */ +#if defined(__CC_ARM) + +#elif defined(__ARMCC_VERSION) && (__ARMCC_VERSION >= 6010050) + +#elif defined(__GNUC__) + +#elif defined(__ICCARM__) + +#elif defined(__TI_ARM__) + +#elif defined(__CSMC__) + +#elif defined(__TASKING__) + +#elif defined(_MSC_VER) + +#else +#error Unknown compiler +#endif + +/* Included for instrinsics definitions */ +#if defined(_MSC_VER) +#include <stdint.h> +#ifndef __STATIC_FORCEINLINE +#define __STATIC_FORCEINLINE static __forceinline +#endif +#ifndef __STATIC_INLINE +#define __STATIC_INLINE static __inline +#endif +#ifndef __ALIGNED +#define __ALIGNED(x) __declspec(align(x)) +#endif + +#elif defined(__GNUC_PYTHON__) +#include <stdint.h> +#ifndef __ALIGNED +#define __ALIGNED(x) __attribute__((aligned(x))) +#endif +#ifndef __STATIC_FORCEINLINE +#define __STATIC_FORCEINLINE static inline __attribute__((always_inline)) +#endif +#ifndef __STATIC_INLINE +#define __STATIC_INLINE static inline +#endif + +#else +#include "cmsis_compiler.h" +#endif + +#include <float.h> +#include <limits.h> +#include <math.h> +#include <string.h> + +/* evaluate ARM DSP feature */ +#if (defined(__ARM_FEATURE_DSP) && (__ARM_FEATURE_DSP == 1)) +#ifndef ARM_MATH_DSP +#define ARM_MATH_DSP 1 +#endif +#endif + +#if __ARM_FEATURE_MVE +#ifndef ARM_MATH_MVEI +#define ARM_MATH_MVEI +#endif +#endif + +/* Compiler specific diagnostic adjustment */ +#if defined(__CC_ARM) + +#elif defined(__ARMCC_VERSION) && (__ARMCC_VERSION >= 6010050) + +#elif defined(__GNUC__) +// #pragma GCC diagnostic pop + +#elif defined(__ICCARM__) + +#elif defined(__TI_ARM__) + +#elif defined(__CSMC__) + +#elif defined(__TASKING__) + +#elif defined(_MSC_VER) + +#else +#error Unknown compiler +#endif + +#ifdef __cplusplus +} +#endif + +#if __ARM_FEATURE_MVE +#include <arm_mve.h> +#endif + +#ifdef __cplusplus +extern "C" { +#endif + +/** + * @brief Add necessary typedefs + */ + +#define NN_Q31_MAX ((q31_t)(0x7FFFFFFFL)) +#define NN_Q15_MAX ((q15_t)(0x7FFF)) +#define NN_Q7_MAX ((q7_t)(0x7F)) +#define NN_Q31_MIN ((q31_t)(0x80000000L)) +#define NN_Q15_MIN ((q15_t)(0x8000)) +#define NN_Q7_MIN ((q7_t)(0x80)) + +/** + * @brief Error status returned by some functions in the library. + */ + +typedef enum +{ + ARM_CMSIS_NN_SUCCESS = 0, /**< No error */ + ARM_CMSIS_NN_ARG_ERROR = -1, /**< One or more arguments are incorrect */ + ARM_CMSIS_NN_NO_IMPL_ERROR = -2, /**< No implementation available */ +} arm_cmsis_nn_status; + +#ifdef __cplusplus +} +#endif + +#endif /*ifndef _ARM_NN_MATH_TYPES_H_ */ diff --git a/Drivers/CMSIS/NN/Include/arm_nn_tables.h b/Drivers/CMSIS/NN/Include/arm_nn_tables.h new file mode 100644 index 0000000..327294d --- /dev/null +++ b/Drivers/CMSIS/NN/Include/arm_nn_tables.h @@ -0,0 +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 */ diff --git a/Drivers/CMSIS/NN/Include/arm_nn_types.h b/Drivers/CMSIS/NN/Include/arm_nn_types.h new file mode 100644 index 0000000..6040d72 --- /dev/null +++ b/Drivers/CMSIS/NN/Include/arm_nn_types.h @@ -0,0 +1,137 @@ +/* + * Copyright (C) 2020-2022 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_nn_types.h + * Description: Public header file to contain the CMSIS-NN structs for the + * TensorFlowLite micro compliant functions + * + * $Date: 22. Februari 2022 + * $Revision: V.2.1.0 + * + * Target Processor: Cortex-M cores + * -------------------------------------------------------------------- */ + +#ifndef _ARM_NN_TYPES_H +#define _ARM_NN_TYPES_H + +#include <stdint.h> + +/** CMSIS-NN object to contain the width and height of a tile */ +typedef struct +{ + int32_t w; /**< Width */ + int32_t h; /**< Height */ +} cmsis_nn_tile; + +/** CMSIS-NN object used for the function context. */ +typedef struct +{ + void *buf; /**< Pointer to a buffer needed for the optimization */ + int32_t size; /**< Buffer size */ +} cmsis_nn_context; + +/** CMSIS-NN object to contain the dimensions of the tensors */ +typedef struct +{ + int32_t n; /**< Generic dimension to contain either the batch size or output channels. + Please refer to the function documentation for more information */ + int32_t h; /**< Height */ + int32_t w; /**< Width */ + int32_t c; /**< Input channels */ +} cmsis_nn_dims; + +/** CMSIS-NN object for the per-channel quantization parameters */ +typedef struct +{ + int32_t *multiplier; /**< Multiplier values */ + int32_t *shift; /**< Shift values */ +} cmsis_nn_per_channel_quant_params; + +/** CMSIS-NN object for the per-tensor quantization parameters */ +typedef struct +{ + int32_t multiplier; /**< Multiplier value */ + int32_t shift; /**< Shift value */ +} cmsis_nn_per_tensor_quant_params; + +/** CMSIS-NN object for the quantized Relu activation */ +typedef struct +{ + int32_t min; /**< Min value used to clamp the result */ + int32_t max; /**< Max value used to clamp the result */ +} cmsis_nn_activation; + +/** CMSIS-NN object for the convolution layer parameters */ +typedef struct +{ + int32_t input_offset; /**< Zero value for the input tensor */ + int32_t output_offset; /**< Zero value for the output tensor */ + cmsis_nn_tile stride; + cmsis_nn_tile padding; + cmsis_nn_tile dilation; + cmsis_nn_activation activation; +} cmsis_nn_conv_params; + +/** CMSIS-NN object for Depthwise convolution layer parameters */ +typedef struct +{ + int32_t input_offset; /**< Zero value for the input tensor */ + int32_t output_offset; /**< Zero value for the output tensor */ + int32_t ch_mult; /**< Channel Multiplier. ch_mult * in_ch = out_ch */ + cmsis_nn_tile stride; + cmsis_nn_tile padding; + cmsis_nn_tile dilation; + cmsis_nn_activation activation; +} cmsis_nn_dw_conv_params; +/** CMSIS-NN object for pooling layer parameters */ +typedef struct +{ + cmsis_nn_tile stride; + cmsis_nn_tile padding; + cmsis_nn_activation activation; +} cmsis_nn_pool_params; + +/** CMSIS-NN object for Fully Connected layer parameters */ +typedef struct +{ + int32_t input_offset; /**< Zero value for the input tensor */ + int32_t filter_offset; /**< Zero value for the filter tensor. Not used */ + int32_t output_offset; /**< Zero value for the output tensor */ + cmsis_nn_activation activation; +} cmsis_nn_fc_params; + +/** CMSIS-NN object for SVDF layer parameters */ +typedef struct +{ + int32_t rank; + int32_t input_offset; /**< Zero value for the input tensor */ + int32_t output_offset; /**< Zero value for the output tensor */ + cmsis_nn_activation input_activation; + cmsis_nn_activation output_activation; +} cmsis_nn_svdf_params; + +/** CMSIS-NN object for Softmax s16 layer parameters */ +typedef struct +{ + const int16_t *exp_lut; + const int16_t *one_by_one_lut; +} cmsis_nn_softmax_lut_s16; + +#endif // _ARM_NN_TYPES_H diff --git a/Drivers/CMSIS/NN/Include/arm_nnfunctions.h b/Drivers/CMSIS/NN/Include/arm_nnfunctions.h new file mode 100644 index 0000000..deaade7 --- /dev/null +++ b/Drivers/CMSIS/NN/Include/arm_nnfunctions.h @@ -0,0 +1,2532 @@ +/* + * 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 diff --git a/Drivers/CMSIS/NN/Include/arm_nnsupportfunctions.h b/Drivers/CMSIS/NN/Include/arm_nnsupportfunctions.h new file mode 100644 index 0000000..4b50564 --- /dev/null +++ b/Drivers/CMSIS/NN/Include/arm_nnsupportfunctions.h @@ -0,0 +1,1186 @@ +/* + * 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 |