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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 |