/* * SPDX-FileCopyrightText: Copyright 2023-2024 Arm Limited and/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_convolve_wrapper_s4.c * Description: s4 convolution layer wrapper function with the main purpose to call the optimal kernel available in * cmsis-nn to perform the convolution. See header files for details. * * $Date: 10 April 2024 * $Revision: V.1.1.0 * * Target : Arm(R) M-Profile Architecture * * -------------------------------------------------------------------- */ #include "arm_nnfunctions.h" /** * @ingroup Public */ /** * @addtogroup NNConv * @{ */ /* * Convolution layer * * Refer header file for details. * */ arm_cmsis_nn_status arm_convolve_wrapper_s4(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 int8_t *input_data, const cmsis_nn_dims *filter_dims, const int8_t *filter_data, const cmsis_nn_dims *bias_dims, const int32_t *bias_data, const cmsis_nn_dims *output_dims, int8_t *output_data) { if ((conv_params->padding.w == 0) && (conv_params->padding.h == 0) && (filter_dims->w == 1) && (filter_dims->h == 1) && (conv_params->dilation.w == 1 && conv_params->dilation.h == 1)) { if ((conv_params->stride.w == 1) && (conv_params->stride.h == 1)) { return arm_convolve_1x1_s4_fast(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_1x1_s4(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) && conv_params->dilation.w == 1 && (filter_dims->h == 1) && ((conv_params->stride.w * input_dims->c) % 4 == 0) && (input_dims->c == filter_dims->c)) { return arm_convolve_1_x_n_s4(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_s4(ctx, conv_params, quant_params, input_dims, input_data, filter_dims, filter_data, bias_dims, bias_data, output_dims, output_data); } } /** * @} end of NNConv group */