/*
* 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
* ARM_MATH_SIZE_MISMATCH if argument constraints fail. or,
* ARM_MATH_SUCCESS 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
* ARM_MATH_SIZE_MISMATCH if argument constraints fail. or,
* ARM_MATH_SUCCESS 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 ARM_MATH_SUCCESS
*
* @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 ARM_MATH_SUCCESS
*
* @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 ARM_MATH_SUCCESS
*
* @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 ARM_MATH_SUCCESS
*
*/
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 ARM_MATH_SUCCESS
*/
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 ARM_MATH_SUCCESS
*
*/
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
* ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS 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
* ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS 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
* ARM_MATH_SIZE_MISMATCH if argument constraints fail. or,
* ARM_MATH_SUCCESS 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
* ARM_MATH_SIZE_MISMATCH if argument constraints fail. or,
* ARM_MATH_SUCCESS 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
* ARM_MATH_SIZE_MISMATCH if argument constraints fail. or,
* ARM_MATH_SUCCESS 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
* ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS 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
* ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS 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
* ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS based on the outcome of size checking.
*
* @details
*
* Buffer size:
*
* bufferA size: 2*ch_im_in*dim_kernel*dim_kernel
*
* bufferB size: 0
*
* Input dimension constraints:
*
* 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
* ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS 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
* ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS 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
* ARM_MATH_SUCCESS - 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 ARM_MATH_SUCCESS
*
* @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 ARM_MATH_SUCCESS
*
* @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
* ARM_MATH_SIZE_MISMATCH - Unsupported dimension of tensors
* ARM_MATH_ARGUMENT_ERROR - Unsupported pad size along the x axis
* ARM_MATH_SUCCESS - 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
* ARM_MATH_SIZE_MISMATCH - input channel != output channel or
* ch_mult != 1
* ARM_MATH_SUCCESS - 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 ARM_MATH_SUCCESS
*
*/
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 ARM_MATH_SUCCESS
*
* @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 ARM_MATH_SUCCESS
*
* @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 ARM_MATH_SUCCESS
*
*/
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 ARM_MATH_SUCCESS
*
*/
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 ARM_MATH_SUCCESS
*
*/
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 ARM_MATH_SUCCESS
*
*/
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 ARM_MATH_SUCCESS
*
*/
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
* ARM_MATH_SUCCESS - 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
* ARM_MATH_SUCCESS - 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
* ARM_MATH_SUCCESS - 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
* ARM_MATH_SUCCESS - 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
* ARM_MATH_ARGUMENT_ERROR if LUTs are NULL
* ARM_MATH_SUCCESS - 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
* ARM_MATH_SUCCESS - 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
*
* Input constraints
* 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
*
* Input constraints
* 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
*
* Input constraints
* 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 ARM_MATH_SUCCESS
*
* @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 ARM_MATH_SUCCESS
*
* @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