1 /* 2 * SPDX-FileCopyrightText: Copyright 2020-2024 Arm Limited and/or its affiliates <open-source-office@arm.com> 3 * 4 * SPDX-License-Identifier: Apache-2.0 5 * 6 * Licensed under the Apache License, Version 2.0 (the License); you may 7 * not use this file except in compliance with the License. 8 * You may obtain a copy of the License at 9 * 10 * www.apache.org/licenses/LICENSE-2.0 11 * 12 * Unless required by applicable law or agreed to in writing, software 13 * distributed under the License is distributed on an AS IS BASIS, WITHOUT 14 * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 15 * See the License for the specific language governing permissions and 16 * limitations under the License. 17 */ 18 19 /* ---------------------------------------------------------------------- 20 * Project: CMSIS NN Library 21 * Title: arm_nn_types.h 22 * Description: Public header file to contain the CMSIS-NN structs for the 23 * TensorFlowLite micro compliant functions 24 * 25 * $Date: 21 Oct 2024 26 * $Revision: V.3.5.0 27 * 28 * Target : Arm(R) M-Profile Architecture 29 * -------------------------------------------------------------------- */ 30 31 #ifndef ARM_NN_TYPES_H 32 #define ARM_NN_TYPES_H 33 34 #include <stdbool.h> 35 #include <stdint.h> 36 37 /** 38 * @defgroup genPubTypes Structure Types 39 * @ingroup Public 40 * @brief Enums and Data Structures used in public API 41 * @{ 42 */ 43 44 /** Enum for specifying activation function types */ 45 typedef enum 46 { 47 ARM_SIGMOID = 0, /**< Sigmoid activation function */ 48 ARM_TANH = 1, /**< Tanh activation function */ 49 } arm_nn_activation_type; 50 51 /** Function return codes */ 52 typedef enum 53 { 54 ARM_CMSIS_NN_SUCCESS = 0, /**< No error */ 55 ARM_CMSIS_NN_ARG_ERROR = -1, /**< One or more arguments are incorrect */ 56 ARM_CMSIS_NN_NO_IMPL_ERROR = -2, /**< No implementation available */ 57 ARM_CMSIS_NN_FAILURE = -3, /**< Logical error */ 58 } arm_cmsis_nn_status; 59 60 /** CMSIS-NN object to contain the width and height of a tile */ 61 typedef struct 62 { 63 int32_t w; /**< Width */ 64 int32_t h; /**< Height */ 65 } cmsis_nn_tile; 66 67 /** CMSIS-NN object used for the function context. */ 68 typedef struct 69 { 70 void *buf; /**< Pointer to a buffer needed for the optimization */ 71 int32_t size; /**< Buffer size */ 72 } cmsis_nn_context; 73 74 /** CMSIS-NN object used to hold bias data for int16 variants. */ 75 typedef struct 76 { 77 const void *data; /**< Pointer to bias data */ 78 const bool is_int32_bias; /**< Indicate type of bias data. True means int32 else int64 */ 79 } cmsis_nn_bias_data; 80 81 /** CMSIS-NN object to contain the dimensions of the tensors */ 82 typedef struct 83 { 84 int32_t n; /**< Generic dimension to contain either the batch size or output channels. 85 Please refer to the function documentation for more information */ 86 int32_t h; /**< Height */ 87 int32_t w; /**< Width */ 88 int32_t c; /**< Input channels */ 89 } cmsis_nn_dims; 90 91 /** CMSIS-NN object to contain LSTM specific input parameters related to dimensions */ 92 typedef struct 93 { 94 int32_t max_time; 95 int32_t num_inputs; 96 int32_t num_batches; 97 int32_t num_outputs; 98 } cmsis_nn_lstm_dims; 99 100 /** CMSIS-NN object for the per-channel quantization parameters */ 101 typedef struct 102 { 103 int32_t *multiplier; /**< Multiplier values */ 104 int32_t *shift; /**< Shift values */ 105 } cmsis_nn_per_channel_quant_params; 106 107 /** CMSIS-NN object for the per-tensor quantization parameters */ 108 typedef struct 109 { 110 int32_t multiplier; /**< Multiplier value */ 111 int32_t shift; /**< Shift value */ 112 } cmsis_nn_per_tensor_quant_params; 113 114 /** CMSIS-NN object for quantization parameters. 115 * This struct supports both per-tensor and per-channels requantization 116 * and is recommended for new operators. 117 */ 118 typedef struct 119 { 120 int32_t *multiplier; /**< Multiplier values */ 121 int32_t *shift; /**< Shift values */ 122 int32_t is_per_channel; /** Indicating if per channel or per tensor quantization */ 123 } cmsis_nn_quant_params; 124 125 /** CMSIS-NN object for the quantized Relu activation */ 126 typedef struct 127 { 128 int32_t min; /**< Min value used to clamp the result */ 129 int32_t max; /**< Max value used to clamp the result */ 130 } cmsis_nn_activation; 131 132 /** CMSIS-NN object for the convolution layer parameters */ 133 typedef struct 134 { 135 int32_t input_offset; /**< The negative of the zero value for the input tensor */ 136 int32_t output_offset; /**< The negative of the zero value for the output tensor */ 137 cmsis_nn_tile stride; 138 cmsis_nn_tile padding; 139 cmsis_nn_tile dilation; 140 cmsis_nn_activation activation; 141 } cmsis_nn_conv_params; 142 143 /** CMSIS-NN object for the transpose convolution layer parameters */ 144 typedef struct 145 { 146 int32_t input_offset; /**< The negative of the zero value for the input tensor */ 147 int32_t output_offset; /**< The negative of the zero value for the output tensor */ 148 cmsis_nn_tile stride; 149 cmsis_nn_tile padding; 150 cmsis_nn_tile padding_offsets; 151 cmsis_nn_tile dilation; 152 cmsis_nn_activation activation; 153 } cmsis_nn_transpose_conv_params; 154 155 /** CMSIS-NN object for the depthwise convolution layer parameters */ 156 typedef struct 157 { 158 int32_t input_offset; /**< The negative of the zero value for the input tensor */ 159 int32_t output_offset; /**< The negative of the zero value for the output tensor */ 160 int32_t ch_mult; /**< Channel Multiplier. ch_mult * in_ch = out_ch */ 161 cmsis_nn_tile stride; 162 cmsis_nn_tile padding; 163 cmsis_nn_tile dilation; 164 cmsis_nn_activation activation; 165 } cmsis_nn_dw_conv_params; 166 167 /** CMSIS-NN object for pooling layer parameters */ 168 typedef struct 169 { 170 cmsis_nn_tile stride; 171 cmsis_nn_tile padding; 172 cmsis_nn_activation activation; 173 } cmsis_nn_pool_params; 174 175 /** CMSIS-NN object for Fully Connected layer parameters */ 176 typedef struct 177 { 178 int32_t input_offset; /**< The negative of the zero value for the input tensor */ 179 int32_t filter_offset; /**< The negative of the zero value for the filter tensor */ 180 int32_t output_offset; /**< The negative of the zero value for the output tensor */ 181 cmsis_nn_activation activation; 182 } cmsis_nn_fc_params; 183 184 /** CMSIS-NN object for Batch Matmul layer parameters */ 185 typedef struct 186 { 187 const bool adj_x; 188 const bool adj_y; 189 cmsis_nn_fc_params fc_params; 190 } cmsis_nn_bmm_params; 191 192 /** CMSIS-NN object for Transpose layer parameters */ 193 typedef struct 194 { 195 const int32_t num_dims; 196 const uint32_t *permutations; /**< The dimensions applied to the input dimensions */ 197 } cmsis_nn_transpose_params; 198 199 /** CMSIS-NN object for SVDF layer parameters */ 200 typedef struct 201 { 202 int32_t rank; 203 int32_t input_offset; /**< The negative of the zero value for the input tensor */ 204 int32_t output_offset; /**< The negative of the zero value for the output tensor */ 205 cmsis_nn_activation input_activation; 206 cmsis_nn_activation output_activation; 207 } cmsis_nn_svdf_params; 208 209 /** CMSIS-NN object for Softmax s16 layer parameters */ 210 typedef struct 211 { 212 const int16_t *exp_lut; 213 const int16_t *one_by_one_lut; 214 } cmsis_nn_softmax_lut_s16; 215 216 /** CMSIS-NN object for quantization parameters */ 217 typedef struct 218 { 219 int32_t multiplier; /**< Multiplier value */ 220 int32_t shift; /**< Shift value */ 221 } cmsis_nn_scaling; 222 223 /** CMSIS-NN object for LSTM gate parameters*/ 224 typedef struct 225 { 226 int32_t input_multiplier; 227 int32_t input_shift; 228 const void *input_weights; 229 const void *input_effective_bias; /**< Bias added with precomputed kernel_sum * lhs_offset*/ 230 231 int32_t hidden_multiplier; 232 int32_t hidden_shift; 233 const void *hidden_weights; 234 const void *hidden_effective_bias; /**< Precomputed kernel_sum * lhs_offset*/ 235 236 const void *bias; 237 arm_nn_activation_type activation_type; 238 } cmsis_nn_lstm_gate; 239 240 /** CMSIS-NN object for LSTM parameters*/ 241 typedef struct 242 { 243 int32_t time_major; /**< 0 if first dimension is batch, else first dimension is time */ 244 int32_t batch_size; 245 int32_t time_steps; 246 int32_t input_size; /**< Size of new data input into the LSTM cell*/ 247 int32_t 248 hidden_size; /**< Size of output from the LSTM cell, used as output and recursively into the next time step*/ 249 250 int32_t input_offset; 251 252 int32_t forget_to_cell_multiplier; 253 int32_t forget_to_cell_shift; 254 int32_t input_to_cell_multiplier; 255 int32_t input_to_cell_shift; 256 int32_t cell_clip; /**< Min/max value of cell output*/ 257 int32_t cell_scale_power; 258 259 int32_t output_multiplier; 260 int32_t output_shift; 261 int32_t output_offset; 262 263 cmsis_nn_lstm_gate forget_gate; 264 cmsis_nn_lstm_gate input_gate; 265 cmsis_nn_lstm_gate cell_gate; 266 cmsis_nn_lstm_gate output_gate; 267 } cmsis_nn_lstm_params; 268 269 /** CMSIS-NN object for LSTM scratch buffers*/ 270 typedef struct 271 { 272 void *temp1; 273 void *temp2; 274 void *cell_state; 275 } cmsis_nn_lstm_context; 276 277 /** 278 * @} // end group genPubTypes 279 */ 280 281 #endif /* ARM_NN_TYPES_H */ 282
