/* * Copyright (C) 2020 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_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: 09. October 2020 * $Revision: V.1.0.1 * * Target Processor: Cortex-M cores * * -------------------------------------------------------------------- */ #include "arm_nnfunctions.h" /** * @ingroup groupNN */ /** * @addtogroup NNConv * @{ */ /* * Convolution layer * * Refer header file for details. * */ 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) { if ((conv_params->padding.w == 0) && (conv_params->padding.h == 0) && (input_dims->c % 4 == 0) && (conv_params->stride.w == 1) && (conv_params->stride.h == 1) && (filter_dims->w == 1) && (filter_dims->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 ((output_dims->h == 1) && (input_dims->h == 1) && (filter_dims->h == 1) && (output_dims->w % 4 == 0) && (input_dims->n == 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) && (input_dims->c % 4 == 0) && (conv_params->stride.w == 1) && (conv_params->stride.h == 1) && (filter_dims->w == 1) && (filter_dims->h == 1)) { return arm_convolve_1x1_s8_fast_get_buffer_size(input_dims); } else if ((output_dims->h == 1) && (input_dims->h == 1) && (filter_dims->h == 1) && (output_dims->w % 4 == 0) && (input_dims->n == 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 */