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Hey-Edge / edge-impulse-sdk /CMSIS /NN /Source /ConvolutionFunctions /arm_convolve_wrapper_s8.c
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#include "edge-impulse-sdk/classifier/ei_classifier_config.h"
#if EI_CLASSIFIER_TFLITE_LOAD_CMSIS_NN_SOURCES
/*
* SPDX-FileCopyrightText: Copyright 2010-2022 Arm Limited and/or its affiliates <open-source-office@arm.com>
*
* 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_convolve_wrapper_s8.c
* Description: s8 convolution layer wrapper function with the main purpose to call the optimal kernel available in
* cmsis-nn to perform the convolution.
*
* $Date: 4 August 2022
* $Revision: V.2.1.1
*
* Target Processor: Cortex-M cores
*
* -------------------------------------------------------------------- */
#include "edge-impulse-sdk/CMSIS/NN/Include/arm_nnfunctions.h"
/**
* @ingroup groupNN
*/
/**
* @addtogroup NNConv
* @{
*/
/*
* Convolution layer
*
* Refer header file for details.
*
*/
arm_cmsis_nn_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)
{
if ((conv_params->padding.w == 0) && (conv_params->padding.h == 0) && (conv_params->stride.w == 1) &&
(conv_params->stride.h == 1) && (filter_dims->w == 1) && (filter_dims->h == 1) &&
(conv_params->dilation.w == 1 && conv_params->dilation.h == 1))
{
return arm_convolve_1x1_s8_fast(ctx,
conv_params,
quant_params,
input_dims,
input_data,
filter_dims,
filter_data,
bias_dims,
bias_data,
output_dims,
output_data);
}
else if ((input_dims->h == 1) && (output_dims->w % 4 == 0) && conv_params->dilation.w == 1 && (filter_dims->h == 1))
{
return arm_convolve_1_x_n_s8(ctx,
conv_params,
quant_params,
input_dims,
input_data,
filter_dims,
filter_data,
bias_dims,
bias_data,
output_dims,
output_data);
}
else
{
return arm_convolve_s8(ctx,
conv_params,
quant_params,
input_dims,
input_data,
filter_dims,
filter_data,
bias_dims,
bias_data,
output_dims,
output_data);
}
}
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)
{
if ((conv_params->padding.w == 0) && (conv_params->padding.h == 0) && (conv_params->stride.w == 1) &&
(conv_params->stride.h == 1) && (filter_dims->w == 1) && (filter_dims->h == 1) &&
(conv_params->dilation.w == 1 && conv_params->dilation.h == 1))
{
return arm_convolve_1x1_s8_fast_get_buffer_size(input_dims);
}
else if ((input_dims->h == 1) && (output_dims->w % 4 == 0) && (conv_params->dilation.w == 1) &&
(filter_dims->h == 1))
{
return arm_convolve_1_x_n_s8_get_buffer_size(input_dims, filter_dims);
}
else
{
return arm_convolve_s8_get_buffer_size(input_dims, filter_dims);
}
}
/**
* @} end of NNConv group
*/
#endif // EI_CLASSIFIER_TFLITE_LOAD_CMSIS_NN_SOURCES