Robert Lopez
Aded CMSIS5 DSP and NN folder. Needs some work
NN/include/arm_nnfunctions.h
- Committer:
- robert_lp
- Date:
- 2018-04-12
- Revision:
- 0:eedb7d567a5d
File content as of revision 0:eedb7d567a5d:
/*
* Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
*
* SPDX-License-Identifier: Apache-2.0
*
* Licensed under the Apache License, Version 2.0 (the License); you may
* not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an AS IS BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
/* ----------------------------------------------------------------------
* Project: CMSIS NN Library
* Title: arm_nnfunctions.h
* Description: Public header file for CMSIS NN Library
*
* $Date: 17. January 2018
* $Revision: V.1.0.0
*
* Target Processor: Cortex-M cores
* -------------------------------------------------------------------- */
/**
\mainpage CMSIS NN Software Library
*
* Introduction
* ------------
*
* This user manual describes the CMSIS NN software library,
* a collection of efficient neural network kernels developed to maximize the
* performance and minimize the memory footprint of neural networks on Cortex-M processor cores.
*
* The library is divided into a number of functions each covering a specific category:
* - Neural Network Convolution Functions
* - Neural Network Activation Functions
* - Fully-connected Layer Functions
* - Neural Network Pooling Functions
* - Softmax Functions
* - Neural Network Support Functions
*
* The library has separate functions for operating on different weight and activation data
* types including 8-bit integers (q7_t) and 16-bit integers (q15_t). The descrition of the
* kernels are included in the function description. The implementation details are also
* described in this paper [1].
*
* Block Diagram
* --------
* \image html CMSIS-NN-OVERVIEW.PNG
*
* Examples
* --------
*
* The library ships with a number of examples which demonstrate how to use the library functions.
*
* Pre-processor Macros
* ------------
*
* Each library project have differant pre-processor macros.
*
* - ARM_MATH_DSP:
*
* Define macro ARM_MATH_DSP, If the silicon supports DSP instructions.
*
* - ARM_MATH_BIG_ENDIAN:
*
* Define macro ARM_MATH_BIG_ENDIAN to build the library for big endian targets. By default library builds for little endian targets.
*
* - ARM_NN_TRUNCATE:
*
* Define macro ARM_NN_TRUNCATE to use floor instead of round-to-the-nearest-int for the computation.
*
* Copyright Notice
* ------------
*
* Copyright (C) 2010-2018 Arm Limited. All rights reserved.
*
* [1] CMSIS-NN: Efficient Neural Network Kernels for Arm Cortex-M CPUs https://arxiv.org/abs/1801.06601
*/
/**
* @defgroup groupNN Neural Network Functions
* These functions perform basic operations for neural network layers.
*/
#ifndef _ARM_NNFUNCTIONS_H
#define _ARM_NNFUNCTIONS_H
#include "arm_nnsupportfunctions.h"
#include "arm_nn_tables.h"
#define USE_INTRINSIC
//#define ARM_NN_TRUNCATE /* This config the rounding model to floor or round to the nearest int */
#ifdef __cplusplus
extern "C"
{
#endif
/**
* @defgroup NNConv Neural Network Convolution Functions
*
* Perform convolution layer
*
* The convolution is implemented in 2 steps: im2col and GEMM
*
* im2col is a process of converting each patch of image data into
* a column. After im2col, the convolution is computed as matrix-matrix
* multiplication.
*
* To reduce the memory footprint, the im2col is performed partially.
* Each iteration, only a few column (i.e., patches) are generated and
* computed with GEMM kernels similar to CMSIS-DSP arm_mat_mult functions.
*
*/
/**
* @brief Basic Q7 convolution function
* @param[in] Im_in pointer to input tensor
* @param[in] dim_im_in input tensor dimention
* @param[in] ch_im_in number of input tensor channels
* @param[in] wt pointer to kernel weights
* @param[in] ch_im_out number of filters, i.e., output tensor channels
* @param[in] dim_kernel filter kernel size
* @param[in] padding padding sizes
* @param[in] stride convolution stride
* @param[in] bias pointer to bias
* @param[in] bias_shift amount of left-shift for bias
* @param[in] out_shift amount of right-shift for output
* @param[in,out] Im_out pointer to output tensor
* @param[in] dim_im_out output tensor dimension
* @param[in,out] bufferA pointer to buffer space for input
* @param[in,out] bufferB pointer to buffer space for output
* @return The function returns <code>ARM_MATH_SUCCESS</code>
*
*/
arm_status arm_convolve_HWC_q7_basic(const q7_t * Im_in,
const uint16_t dim_im_in,
const uint16_t ch_im_in,
const q7_t * wt,
const uint16_t ch_im_out,
const uint16_t dim_kernel,
const uint16_t padding,
const uint16_t stride,
const q7_t * bias,
const uint16_t bias_shift,
const uint16_t out_shift,
q7_t * Im_out,
const uint16_t dim_im_out,
q15_t * bufferA,
q7_t * bufferB);
/**
* @brief Basic Q15 convolution function
* @param[in] Im_in pointer to input tensor
* @param[in] dim_im_in input tensor dimention
* @param[in] ch_im_in number of input tensor channels
* @param[in] wt pointer to kernel weights
* @param[in] ch_im_out number of filters, i.e., output tensor channels
* @param[in] dim_kernel filter kernel size
* @param[in] padding padding sizes
* @param[in] stride convolution stride
* @param[in] bias pointer to bias
* @param[in] bias_shift amount of left-shift for bias
* @param[in] out_shift amount of right-shift for output
* @param[in,out] Im_out pointer to output tensor
* @param[in] dim_im_out output tensor dimension
* @param[in,out] bufferA pointer to buffer space for input
* @param[in,out] bufferB pointer to buffer space for output
* @return The function returns <code>ARM_MATH_SUCCESS</code>
*
*/
arm_status arm_convolve_HWC_q15_basic(const q15_t * Im_in,
const uint16_t dim_im_in,
const uint16_t ch_im_in,
const q15_t * wt,
const uint16_t ch_im_out,
const uint16_t dim_kernel,
const uint16_t padding,
const uint16_t stride,
const q15_t * bias,
const uint16_t bias_shift,
const uint16_t out_shift,
q15_t * Im_out,
const uint16_t dim_im_out,
q15_t * bufferA,
q7_t * bufferB);
/**
* @brief Fast Q7 convolution function
* @param[in] Im_in pointer to input tensor
* @param[in] dim_im_in input tensor dimention
* @param[in] ch_im_in number of input tensor channels
* @param[in] wt pointer to kernel weights
* @param[in] ch_im_out number of filters, i.e., output tensor channels
* @param[in] dim_kernel filter kernel size
* @param[in] padding padding sizes
* @param[in] stride convolution stride
* @param[in] bias pointer to bias
* @param[in] bias_shift amount of left-shift for bias
* @param[in] out_shift amount of right-shift for output
* @param[in,out] Im_out pointer to output tensor
* @param[in] dim_im_out output tensor dimension
* @param[in,out] bufferA pointer to buffer space for input
* @param[in,out] bufferB pointer to buffer space for output
* @return The function returns either
* <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
*
* This function is the version with full list of optimization tricks, but with
* some contraints:
* ch_im_in is multiple of 4
* ch_im_out is multiple of 2
*/
arm_status arm_convolve_HWC_q7_fast(const q7_t * Im_in,
const uint16_t dim_im_in,
const uint16_t ch_im_in,
const q7_t * wt,
const uint16_t ch_im_out,
const uint16_t dim_kernel,
const uint16_t padding,
const uint16_t stride,
const q7_t * bias,
const uint16_t bias_shift,
const uint16_t out_shift,
q7_t * Im_out,
const uint16_t dim_im_out,
q15_t * bufferA,
q7_t * bufferB);
/**
* @brief Fast Q7 convolution function (non-sqaure shape)
* @param[in] Im_in pointer to input tensor
* @param[in] dim_im_in_x input tensor dimention x
* @param[in] dim_im_in_y input tensor dimention y
* @param[in] ch_im_in number of input tensor channels
* @param[in] wt pointer to kernel weights
* @param[in] ch_im_out number of filters, i.e., output tensor channels
* @param[in] dim_kernel_x filter kernel size x
* @param[in] dim_kernel_y filter kernel size y
* @param[in] padding_x padding size x
* @param[in] padding_y padding size y
* @param[in] stride_x convolution stride x
* @param[in] stride_y convolution stride y
* @param[in] bias pointer to bias
* @param[in] bias_shift amount of left-shift for bias
* @param[in] out_shift amount of right-shift for output
* @param[in,out] Im_out pointer to output tensor
* @param[in] dim_im_out_x output tensor dimension x
* @param[in] dim_im_out_y output tensor dimension y
* @param[in,out] bufferA pointer to buffer space for input
* @param[in,out] bufferB pointer to buffer space for output
* @return The function returns either
* <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
*
* This function is the version with full list of optimization tricks, but with
* some contraints:
* ch_im_in is multiple of 4
* ch_im_out is multiple of 2
*/
arm_status arm_convolve_HWC_q7_fast_nonsquare(const q7_t * Im_in,
const uint16_t dim_im_in_x,
const uint16_t dim_im_in_y,
const uint16_t ch_im_in,
const q7_t * wt,
const uint16_t ch_im_out,
const uint16_t dim_kernel_x,
const uint16_t dim_kernel_y,
const uint16_t padding_x,
const uint16_t padding_y,
const uint16_t stride_x,
const uint16_t stride_y,
const q7_t * bias,
const uint16_t bias_shift,
const uint16_t out_shift,
q7_t * Im_out,
const uint16_t dim_im_out_x,
const uint16_t dim_im_out_y,
q15_t * bufferA,
q7_t * bufferB);
/**
* @brief Fast Q7 version of 1x1 convolution (non-sqaure shape)
* @param[in] Im_in pointer to input tensor
* @param[in] dim_im_in_x input tensor dimention x
* @param[in] dim_im_in_y input tensor dimention y
* @param[in] ch_im_in number of input tensor channels
* @param[in] wt pointer to kernel weights
* @param[in] ch_im_out number of filters, i.e., output tensor channels
* @param[in] dim_kernel_x filter kernel size x
* @param[in] dim_kernel_y filter kernel size y
* @param[in] padding_x padding size x
* @param[in] padding_y padding size y
* @param[in] stride_x convolution stride x
* @param[in] stride_y convolution stride y
* @param[in] bias pointer to bias
* @param[in] bias_shift amount of left-shift for bias
* @param[in] out_shift amount of right-shift for output
* @param[in,out] Im_out pointer to output tensor
* @param[in] dim_im_out_x output tensor dimension x
* @param[in] dim_im_out_y output tensor dimension y
* @param[in,out] bufferA pointer to buffer space for input
* @param[in,out] bufferB pointer to buffer space for output
* @return The function returns either
* <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
*
* This function implement convolution with 1x1 kernel size (i.e., dim_kernel_x=1
* and dim_kernel_y=1). It can be used for
* second half of MobileNets after depthwise separable convolution.
*
* This function is the version with full list of optimization tricks, but with
* some contraints:
* ch_im_in is multiple of 4
* ch_im_out is multiple of 2
*/
arm_status arm_convolve_1x1_HWC_q7_fast_nonsquare(const q7_t * Im_in,
const uint16_t dim_im_in_x,
const uint16_t dim_im_in_y,
const uint16_t ch_im_in,
const q7_t * wt,
const uint16_t ch_im_out,
const uint16_t dim_kernel_x,
const uint16_t dim_kernel_y,
const uint16_t padding_x,
const uint16_t padding_y,
const uint16_t stride_x,
const uint16_t stride_y,
const q7_t * bias,
const uint16_t bias_shift,
const uint16_t out_shift,
q7_t * Im_out,
const uint16_t dim_im_out_x,
const uint16_t dim_im_out_y,
q15_t * bufferA,
q7_t * bufferB);
/**
* @brief Q7 version of convolution for RGB image
* @param[in] Im_in pointer to input tensor
* @param[in] dim_im_in input tensor dimention
* @param[in] ch_im_in number of input tensor channels
* @param[in] wt pointer to kernel weights
* @param[in] ch_im_out number of filters, i.e., output tensor channels
* @param[in] dim_kernel filter kernel size
* @param[in] padding padding sizes
* @param[in] stride convolution stride
* @param[in] bias pointer to bias
* @param[in] bias_shift amount of left-shift for bias
* @param[in] out_shift amount of right-shift for output
* @param[in,out] Im_out pointer to output tensor
* @param[in] dim_im_out output tensor dimension
* @param[in,out] bufferA pointer to buffer space for input
* @param[in,out] bufferB pointer to buffer space for output
* @return The function returns either
* <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
*
* This kernel is written exclusively for convolution with ch_im_in
* equals 3. This applies on the first layer of CNNs which has input
* image with RGB format.
*/
arm_status arm_convolve_HWC_q7_RGB(const q7_t * Im_in,
const uint16_t dim_im_in,
const uint16_t ch_im_in,
const q7_t * wt,
const uint16_t ch_im_out,
const uint16_t dim_kernel,
const uint16_t padding,
const uint16_t stride,
const q7_t * bias,
const uint16_t bias_shift,
const uint16_t out_shift,
q7_t * Im_out,
const uint16_t dim_im_out,
q15_t * bufferA,
q7_t * bufferB);
/**
* @brief Fast Q15 convolution function
* @param[in] Im_in pointer to input tensor
* @param[in] dim_im_in input tensor dimention
* @param[in] ch_im_in number of input tensor channels
* @param[in] wt pointer to kernel weights
* @param[in] ch_im_out number of filters, i.e., output tensor channels
* @param[in] dim_kernel filter kernel size
* @param[in] padding padding sizes
* @param[in] stride convolution stride
* @param[in] bias pointer to bias
* @param[in] bias_shift amount of left-shift for bias
* @param[in] out_shift amount of right-shift for output
* @param[in,out] Im_out pointer to output tensor
* @param[in] dim_im_out output tensor dimension
* @param[in,out] bufferA pointer to buffer space for input
* @param[in,out] bufferB pointer to buffer space for output
* @return The function returns either
* <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
*
* This function is the version with full list of optimization tricks, but with
* some contraints:
* ch_im_in is multiple of 2
* ch_im_out is multiple of 2
*/
arm_status arm_convolve_HWC_q15_fast(const q15_t * Im_in,
const uint16_t dim_im_in,
const uint16_t ch_im_in,
const q15_t * wt,
const uint16_t ch_im_out,
const uint16_t dim_kernel,
const uint16_t padding,
const uint16_t stride,
const q15_t * bias,
const uint16_t bias_shift,
const uint16_t out_shift,
q15_t * Im_out,
const uint16_t dim_im_out,
q15_t * bufferA,
q7_t * bufferB);
/**
* @brief Q7 depthwise separable convolution function
* @param[in] Im_in pointer to input tensor
* @param[in] dim_im_in input tensor dimention
* @param[in] ch_im_in number of input tensor channels
* @param[in] wt pointer to kernel weights
* @param[in] ch_im_out number of filters, i.e., output tensor channels
* @param[in] dim_kernel filter kernel size
* @param[in] padding padding sizes
* @param[in] stride convolution stride
* @param[in] bias pointer to bias
* @param[in] bias_shift amount of left-shift for bias
* @param[in] out_shift amount of right-shift for output
* @param[in,out] Im_out pointer to output tensor
* @param[in] dim_im_out output tensor dimension
* @param[in,out] bufferA pointer to buffer space for input
* @param[in,out] bufferB pointer to buffer space for output
* @return The function returns either
* <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
*
* This function is the version with full list of optimization tricks, but with
* some contraints:
* ch_im_in is multiple of 2
* ch_im_out is multiple of 2
*/
arm_status arm_depthwise_separable_conv_HWC_q7(const q7_t * Im_in,
const uint16_t dim_im_in,
const uint16_t ch_im_in,
const q7_t * wt,
const uint16_t ch_im_out,
const uint16_t dim_kernel,
const uint16_t padding,
const uint16_t stride,
const q7_t * bias,
const uint16_t bias_shift,
const uint16_t out_shift,
q7_t * Im_out,
const uint16_t dim_im_out,
q15_t * bufferA,
q7_t * bufferB);
/**
* @brief Q7 depthwise separable convolution function (non-square shape)
* @param[in] Im_in pointer to input tensor
* @param[in] dim_im_in_x input tensor dimention x
* @param[in] dim_im_in_y input tensor dimention y
* @param[in] ch_im_in number of input tensor channels
* @param[in] wt pointer to kernel weights
* @param[in] ch_im_out number of filters, i.e., output tensor channels
* @param[in] dim_kernel_x filter kernel size x
* @param[in] dim_kernel_y filter kernel size y
* @param[in] padding_x padding sizes x
* @param[in] padding_y padding sizes y
* @param[in] stride_x convolution stride x
* @param[in] stride_y convolution stride y
* @param[in] bias pointer to bias
* @param[in] bias_shift amount of left-shift for bias
* @param[in] out_shift amount of right-shift for output
* @param[in,out] Im_out pointer to output tensor
* @param[in] dim_im_out_x output tensor dimension x
* @param[in] dim_im_out_y output tensor dimension y
* @param[in,out] bufferA pointer to buffer space for input
* @param[in,out] bufferB pointer to buffer space for output
* @return The function returns either
* <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
*
* This function is the version with full list of optimization tricks, but with
* some contraints:
* ch_im_in is multiple of 2
* ch_im_out is multiple of 2
*/
arm_status arm_depthwise_separable_conv_HWC_q7_nonsquare(const q7_t * Im_in,
const uint16_t dim_im_in_x,
const uint16_t dim_im_in_y,
const uint16_t ch_im_in,
const q7_t * wt,
const uint16_t ch_im_out,
const uint16_t dim_kernel_x,
const uint16_t dim_kernel_y,
const uint16_t padding_x,
const uint16_t padding_y,
const uint16_t stride_x,
const uint16_t stride_y,
const q7_t * bias,
const uint16_t bias_shift,
const uint16_t out_shift,
q7_t * Im_out,
const uint16_t dim_im_out_x,
const uint16_t dim_im_out_y,
q15_t * bufferA,
q7_t * bufferB);
/**
* @defgroup FC Fully-connected Layer Functions
*
* Perform fully-connected layer
*
* Fully-connected layer is basically a matrix-vector multiplication
* with bias. The matrix is the weights and the input/output vectors
* are the activation values. Supported {weight, activation} precisions
* include {8-bit, 8-bit}, {16-bit, 16-bit}, and {8-bit, 16-bit}.
*
* Here we have two types of kernel functions. The basic function
* implements the function using regular GEMV approach. The opt functions
* operates with weights in interleaved formats.
*
*/
/**
* @brief Q7 basic fully-connected layer function
* @param[in] pV pointer to input vector
* @param[in] pM pointer to matrix weights
* @param[in] dim_vec length of the vector
* @param[in] num_of_rows number of rows in weight matrix
* @param[in] bias_shift amount of left-shift for bias
* @param[in] out_shift amount of right-shift for output
* @param[in] bias pointer to bias
* @param[in,out] pOut pointer to output vector
* @param[in,out] vec_buffer pointer to buffer space for input
* @return The function returns <code>ARM_MATH_SUCCESS</code>
*
*/
arm_status arm_fully_connected_q7(const q7_t * pV,
const q7_t * pM,
const uint16_t dim_vec,
const uint16_t num_of_rows,
const uint16_t bias_shift,
const uint16_t out_shift,
const q7_t * bias,
q7_t * pOut,
q15_t * vec_buffer);
/**
* @brief Q7 opt fully-connected layer function
* @param[in] pV pointer to input vector
* @param[in] pM pointer to matrix weights
* @param[in] dim_vec length of the vector
* @param[in] num_of_rows number of rows in weight matrix
* @param[in] bias_shift amount of left-shift for bias
* @param[in] out_shift amount of right-shift for output
* @param[in] bias pointer to bias
* @param[in,out] pOut pointer to output vector
* @param[in,out] vec_buffer pointer to buffer space for input
* @return The function returns <code>ARM_MATH_SUCCESS</code>
*
*/
arm_status arm_fully_connected_q7_opt(const q7_t * pV,
const q7_t * pM,
const uint16_t dim_vec,
const uint16_t num_of_rows,
const uint16_t bias_shift,
const uint16_t out_shift,
const q7_t * bias,
q7_t * pOut,
q15_t * vec_buffer);
/**
* @brief Q15 basic fully-connected layer function
* @param[in] pV pointer to input vector
* @param[in] pM pointer to matrix weights
* @param[in] dim_vec length of the vector
* @param[in] num_of_rows number of rows in weight matrix
* @param[in] bias_shift amount of left-shift for bias
* @param[in] out_shift amount of right-shift for output
* @param[in] bias pointer to bias
* @param[in,out] pOut pointer to output vector
* @param[in,out] vec_buffer pointer to buffer space for input
* @return The function returns <code>ARM_MATH_SUCCESS</code>
*
*/
arm_status arm_fully_connected_q15(const q15_t * pV,
const q15_t * pM,
const uint16_t dim_vec,
const uint16_t num_of_rows,
const uint16_t bias_shift,
const uint16_t out_shift,
const q15_t * bias,
q15_t * pOut,
q15_t * vec_buffer);
/**
* @brief Q15 opt fully-connected layer function
* @param[in] pV pointer to input vector
* @param[in] pM pointer to matrix weights
* @param[in] dim_vec length of the vector
* @param[in] num_of_rows number of rows in weight matrix
* @param[in] bias_shift amount of left-shift for bias
* @param[in] out_shift amount of right-shift for output
* @param[in] bias pointer to bias
* @param[in,out] pOut pointer to output vector
* @param[in,out] vec_buffer pointer to buffer space for input
* @return The function returns <code>ARM_MATH_SUCCESS</code>
*
*/
arm_status arm_fully_connected_q15_opt(const q15_t * pV,
const q15_t * pM,
const uint16_t dim_vec,
const uint16_t num_of_rows,
const uint16_t bias_shift,
const uint16_t out_shift,
const q15_t * bias,
q15_t * pOut,
q15_t * vec_buffer);
/**
* @brief Mixed Q15-Q7 fully-connected layer function
* @param[in] pV pointer to input vector
* @param[in] pM pointer to matrix weights
* @param[in] dim_vec length of the vector
* @param[in] num_of_rows number of rows in weight matrix
* @param[in] bias_shift amount of left-shift for bias
* @param[in] out_shift amount of right-shift for output
* @param[in] bias pointer to bias
* @param[in,out] pOut pointer to output vector
* @param[in,out] vec_buffer pointer to buffer space for input
* @return The function returns <code>ARM_MATH_SUCCESS</code>
*
*/
arm_status arm_fully_connected_mat_q7_vec_q15(const q15_t * pV,
const q7_t * pM,
const uint16_t dim_vec,
const uint16_t num_of_rows,
const uint16_t bias_shift,
const uint16_t out_shift,
const q7_t * bias,
q15_t * pOut,
q15_t * vec_buffer);
/**
* @brief Mixed Q15-Q7 opt fully-connected layer function
* @param[in] pV pointer to input vector
* @param[in] pM pointer to matrix weights
* @param[in] dim_vec length of the vector
* @param[in] num_of_rows number of rows in weight matrix
* @param[in] bias_shift amount of left-shift for bias
* @param[in] out_shift amount of right-shift for output
* @param[in] bias pointer to bias
* @param[in,out] pOut pointer to output vector
* @param[in,out] vec_buffer pointer to buffer space for input
* @return The function returns <code>ARM_MATH_SUCCESS</code>
*
*/
arm_status arm_fully_connected_mat_q7_vec_q15_opt(const q15_t * pV,
const q7_t * pM,
const uint16_t dim_vec,
const uint16_t num_of_rows,
const uint16_t bias_shift,
const uint16_t out_shift,
const q7_t * bias,
q15_t * pOut,
q15_t * vec_buffer);
/**
* @brief Matrix-Multiplication Kernels for Convolution
*
* These functions are used within convolution layer functions for
* matrix multiplication.
*
* The implementation is similar to CMSIS-DSP arm_mat_mult functions
* with one Q7 and one Q15 operands. The Q15 operand is the im2col
* output which is always with 2 columns.
*
*/
/**
* @brief Matrix-multiplication function for convolution
* @param[in] pA pointer to operand A
* @param[in] pInBuffer pointer to operand B, always conssists of 2 vectors
* @param[in] ch_im_out numRow of A
* @param[in] numCol_A numCol of A
* @param[in] bias_shift amount of left-shift for bias
* @param[in] out_shift amount of right-shift for output
* @param[in] bias the bias
* @param[in,out] pOut pointer to output
* @return The function returns the incremented output pointer
*/
q7_t *arm_nn_mat_mult_kernel_q7_q15(const q7_t * pA,
const q15_t * pInBuffer,
const uint16_t ch_im_out,
const uint16_t numCol_A,
const uint16_t bias_shift,
const uint16_t out_shift,
const q7_t * bias,
q7_t * pOut);
/**
* @brief Matrix-multiplication function for convolution with reordered columns
* @param[in] pA pointer to operand A
* @param[in] pInBuffer pointer to operand B, always conssists of 2 vectors
* @param[in] ch_im_out numRow of A
* @param[in] numCol_A numCol of A
* @param[in] bias_shift amount of left-shift for bias
* @param[in] out_shift amount of right-shift for output
* @param[in] bias the bias
* @param[in,out] pOut pointer to output
* @return The function returns the incremented output pointer
*/
q7_t *arm_nn_mat_mult_kernel_q7_q15_reordered(const q7_t * pA,
const q15_t * pInBuffer,
const uint16_t ch_im_out,
const uint16_t numCol_A,
const uint16_t bias_shift,
const uint16_t out_shift,
const q7_t * bias,
q7_t * pOut);
#ifdef __cplusplus
}
#endif
/*
* Other functions
* These layers are typically not timing critical
* Basic implementation is supported here
*/
#ifdef __cplusplus
extern "C"
{
#endif
/**
* @defgroup Acti Neural Network Activation Functions
*
* Perform activation layers, including ReLU (Rectified Linear Unit),
* sigmoid and tanh
*
*/
/**
* @brief Q7 RELU function
* @param[in,out] data pointer to input
* @param[in] size number of elements
* @return none.
*/
void arm_relu_q7(q7_t * data, uint16_t size);
/**
* @brief Q15 RELU function
* @param[in,out] data pointer to input
* @param[in] size number of elements
* @return none.
*/
void arm_relu_q15(q15_t * data, uint16_t size);
/**
* @brief Q7 neural network activation function using direct table look-up
* @param[in,out] data pointer to input
* @param[in] size number of elements
* @param[in] int_width bit-width of the integer part, assume to be smaller than 3
* @param[in] type type of activation functions
* @return none.
*/
void arm_nn_activations_direct_q7(q7_t * data, uint16_t size, uint16_t int_width,
arm_nn_activation_type type);
/**
* @brief Q15 neural network activation function using direct table look-up
* @param[in,out] data pointer to input
* @param[in] size number of elements
* @param[in] int_width bit-width of the integer part, assume to be smaller than 3
* @param[in] type type of activation functions
* @return none.
*/
void arm_nn_activations_direct_q15(q15_t * data, uint16_t size, uint16_t int_width,
arm_nn_activation_type type);
/**
* @defgroup Pooling Neural Network Pooling Functions
*
* Perform pooling functions, including max pooling and average pooling
*
*/
/**
* @brief Q7 max pooling function
* @param[in] Im_in pointer to input tensor
* @param[in] dim_im_in input tensor dimention
* @param[in] ch_im_in number of input tensor channels
* @param[in] dim_kernel filter kernel size
* @param[in] padding padding sizes
* @param[in] stride convolution stride
* @param[in] dim_im_out output tensor dimension
* @param[in,out] bufferA pointer to buffer space for input
* @param[in,out] Im_out pointer to output tensor
* @return none.
*
*/
void arm_maxpool_q7_HWC(q7_t * Im_in,
const uint16_t dim_im_in,
const uint16_t ch_im_in,
const uint16_t dim_kernel,
const uint16_t padding,
const uint16_t stride,
const uint16_t dim_im_out,
q7_t * bufferA,
q7_t * Im_out);
/**
* @brief Q7 average pooling function
* @param[in] Im_in pointer to input tensor
* @param[in] dim_im_in input tensor dimention
* @param[in] ch_im_in number of input tensor channels
* @param[in] dim_kernel filter kernel size
* @param[in] padding padding sizes
* @param[in] stride convolution stride
* @param[in] dim_im_out output tensor dimension
* @param[in,out] bufferA pointer to buffer space for input
* @param[in,out] Im_out pointer to output tensor
* @return none.
*
*/
void arm_avepool_q7_HWC(q7_t * Im_in,
const uint16_t dim_im_in,
const uint16_t ch_im_in,
const uint16_t dim_kernel,
const uint16_t padding,
const uint16_t stride,
const uint16_t dim_im_out,
q7_t * bufferA,
q7_t * Im_out);
/**
* @defgroup Softmax Softmax Functions
*
* EXP(2) based softmax function
*
*/
/**
* @brief Q7 softmax function
* @param[in] vec_in pointer to input vector
* @param[in] dim_vec input vector dimention
* @param[out] p_out pointer to output vector
* @return none.
*
*/
void arm_softmax_q7(const q7_t * vec_in, const uint16_t dim_vec, q7_t * p_out);
/**
* @brief Q15 softmax function
* @param[in] vec_in pointer to input vector
* @param[in] dim_vec input vector dimention
* @param[out] p_out pointer to output vector
* @return none.
*
*/
void arm_softmax_q15(const q15_t * vec_in, const uint16_t dim_vec, q15_t * p_out);
#ifdef __cplusplus
}
#endif
#endif