diff options
Diffstat (limited to 'Drivers/CMSIS/NN/Source/SVDFunctions/arm_svdf_s8.c')
| -rw-r--r-- | Drivers/CMSIS/NN/Source/SVDFunctions/arm_svdf_s8.c | 271 |
1 files changed, 271 insertions, 0 deletions
diff --git a/Drivers/CMSIS/NN/Source/SVDFunctions/arm_svdf_s8.c b/Drivers/CMSIS/NN/Source/SVDFunctions/arm_svdf_s8.c new file mode 100644 index 0000000..ea092dc --- /dev/null +++ b/Drivers/CMSIS/NN/Source/SVDFunctions/arm_svdf_s8.c @@ -0,0 +1,271 @@ +/* + * 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_svdf_s8.c + * Description: S8 basic SVDF layer function + * + * $Date: 28 April 2022 + * $Revision: V.3.0.1 + * + * Target Processor: Cortex-M processors + * + * -------------------------------------------------------------------- */ + +#include "arm_nnfunctions.h" +#include "arm_nnsupportfunctions.h" + +/** + * @ingroup groupNN + */ + +/** + * @addtogroup SVDF + * @{ + */ + +/* + * S8 SVDF layer function for TensorFlow Lite with 8 bit state tensor + * + * Refer to header file for details. + * + */ + +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) +{ + (void)bias_dims; + (void)state_dims; + (void)output_dims; + + const q31_t multiplier_in = input_quant_params->multiplier; + const q31_t shift_in = input_quant_params->shift; + const q31_t multiplier_out = output_quant_params->multiplier; + const q31_t shift_2 = output_quant_params->shift; + const int32_t zp_in = svdf_params->input_offset; + const int32_t zp_out = svdf_params->output_offset; + const int32_t in_activation_min = svdf_params->input_activation.min; + const int32_t in_activation_max = svdf_params->input_activation.max; + const int32_t out_activation_min = svdf_params->output_activation.min; + const int32_t out_activation_max = svdf_params->output_activation.max; + const int16_t rank = svdf_params->rank; + + const int32_t input_batches = input_dims->n; + const int32_t input_height = input_dims->h; + const int32_t feature_batches = weights_feature_dims->n; + const int32_t time_batches = weights_time_dims->h; + const int32_t unit_count = feature_batches / rank; + + if (input_ctx->buf == NULL) + { + return ARM_MATH_ARGUMENT_ERROR; + } + q31_t *buffer_a = (q31_t *)input_ctx->buf; + + if (output_ctx->buf == NULL) + { + return ARM_MATH_ARGUMENT_ERROR; + } + q31_t *buffer_b = (q31_t *)output_ctx->buf; + + // Left shift state + memmove((int8_t *)state_data, + (int8_t *)state_data + 1, + (size_t)((input_batches * feature_batches * time_batches - 1) * (int32_t)sizeof(int8_t))); + + // Matrix multiplication input * feature weight + for (int i_batch = 0; i_batch < input_batches; i_batch++) + { + q7_t *res_ptr = state_data + (time_batches * i_batch * feature_batches) + (time_batches - 1); + const q7_t *weight = weights_feature_data; + const q7_t *input = input_data + i_batch * input_height; + + arm_status res = arm_nn_vec_mat_mult_t_s8(input, + weight, + NULL, + res_ptr, + -zp_in, + 0, + 0, + multiplier_in, + shift_in, + input_height, + feature_batches, + in_activation_min, + in_activation_max, + time_batches); + + if (res != ARM_MATH_SUCCESS) + { + return res; + } + } + + // Matrix multiplicate time weight * state tensors + { + q31_t *ptr_a = buffer_a; + const int8_t *v2 = state_data; + for (int i_batch = 0; i_batch < input_batches; i_batch++) + { + const int8_t *v1 = weights_time_data; + + for (int i_feature_batch = 0; i_feature_batch < feature_batches; i_feature_batch++) + { + *ptr_a = 0; + int32_t sum = 0; +#if defined(ARM_MATH_DSP) && !defined(ARM_MATH_MVEI) + // Perform matrix multiplication in blocks of four + int j = 0; + int32_t block_count = time_batches >> 2; + for (int i = 0; i < block_count; i++) + { + j += 4; + + q31_t r1_1, r1_2, r2_1, r2_2; + v1 = read_and_pad_reordered(v1, &r1_1, &r1_2); + v2 = read_and_pad_reordered(v2, &r2_1, &r2_2); + sum = __SMLAD(r1_1, r2_1, sum); + sum = __SMLAD(r1_2, r2_2, sum); + } + + // Process the remaining data + for (; j < time_batches; j++) + { + sum += *v1 * *v2; + v1++; + v2++; + } +#else + for (int j = 0; j < time_batches; j++) + { + sum += *v1 * *v2; + v1++; + v2++; + } +#endif + + *ptr_a = sum; + ptr_a++; + } + } + } + + if (bias_data) + { + if (unit_count == feature_batches) + { + for (int i = 0; i < input_batches; i++) + { + q31_t *output_temp = buffer_b + i * feature_batches; + const q31_t *ptr_a = buffer_a + i * feature_batches; + + const int32_t *bi = bias_data; + for (int j = 0; j < feature_batches; j++) + { + output_temp[j] = ptr_a[j] + bi[j]; + } + } + } + else + { + for (int i_batch = 0; i_batch < input_batches; i_batch++) + { + q31_t *output_data_temp = buffer_b + i_batch * unit_count; + q31_t *ptr_a = buffer_a + i_batch * feature_batches; + + for (int i = 0; i < unit_count; i++) + { + int32_t sum = bias_data[i]; + for (int j = 0; j < rank; j++) + { + sum += *ptr_a; + ptr_a++; + } + output_data_temp[i] = sum; + } + } + } + } + else + { + for (int i_batch = 0; i_batch < input_batches; i_batch++) + { + q31_t *output_data_temp = buffer_b + i_batch * unit_count; + q31_t *ptr_a = buffer_a + i_batch * feature_batches; + + for (int i = 0; i < unit_count; i++) + { + int32_t sum = 0; + for (int j = 0; j < rank; j++) + { + sum += *ptr_a; + ptr_a++; + } + output_data_temp[i] = sum; + } + } + } + +#if defined(ARM_MATH_MVEI) + int32_t num_elements = input_batches * unit_count; + const int32_t loop_count = (num_elements + 3) / 4; + for (int i_op = 0; i_op < loop_count; i_op++) + { + mve_pred16_t p = vctp32q((uint32_t)num_elements); + int32x4_t op = vldrwq_z_s32(buffer_b, p); + op = arm_requantize_mve(op, multiplier_out, shift_2); + op = vaddq_n_s32(op, zp_out); + const int32x4_t min_vec = vdupq_n_s32((int8_t)out_activation_min); + const int32x4_t max_vec = vdupq_n_s32((int8_t)out_activation_max); + op = vmaxq_s32(op, min_vec); + op = vminq_s32(op, max_vec); + vstrbq_p_s32(output_data, op, p); + output_data += 4; + buffer_b += 4; + num_elements -= 4; + } +#else + for (int i = 0; i < input_batches * unit_count; i++) + { + output_data[i] = (q7_t)CLAMP( + arm_nn_requantize(buffer_b[i], multiplier_out, shift_2) + zp_out, out_activation_max, out_activation_min); + } +#endif + + return (ARM_MATH_SUCCESS); +} + +/** + * @} end of SVDF group + */ |