1 /* 2 * SPDX-FileCopyrightText: Copyright 2020-2023 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: 8 March 2023 26 * $Revision: V.2.5.0 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 <stdint.h> 35 36 /** Enum for specifying activation function types */ 37 typedef enum 38 { 39 ARM_SIGMOID = 0, /**< Sigmoid activation function */ 40 ARM_TANH = 1, /**< Tanh activation function */ 41 } arm_nn_activation_type; 42 43 /** Function return codes */ 44 typedef enum 45 { 46 ARM_CMSIS_NN_SUCCESS = 0, /**< No error */ 47 ARM_CMSIS_NN_ARG_ERROR = -1, /**< One or more arguments are incorrect */ 48 ARM_CMSIS_NN_NO_IMPL_ERROR = -2, /**< No implementation available */ 49 ARM_CMSIS_NN_FAILURE = -3, /**< Logical error */ 50 } arm_cmsis_nn_status; 51 52 /** CMSIS-NN object to contain the width and height of a tile */ 53 typedef struct 54 { 55 int32_t w; /**< Width */ 56 int32_t h; /**< Height */ 57 } cmsis_nn_tile; 58 59 /** CMSIS-NN object used for the function context. */ 60 typedef struct 61 { 62 void *buf; /**< Pointer to a buffer needed for the optimization */ 63 int32_t size; /**< Buffer size */ 64 } cmsis_nn_context; 65 66 /** CMSIS-NN object to contain the dimensions of the tensors */ 67 typedef struct 68 { 69 int32_t n; /**< Generic dimension to contain either the batch size or output channels. 70 Please refer to the function documentation for more information */ 71 int32_t h; /**< Height */ 72 int32_t w; /**< Width */ 73 int32_t c; /**< Input channels */ 74 } cmsis_nn_dims; 75 76 /** CMSIS-NN object to contain LSTM specific input parameters related to dimensions */ 77 typedef struct 78 { 79 int32_t max_time; 80 int32_t num_inputs; 81 int32_t num_batches; 82 int32_t num_outputs; 83 } cmsis_nn_lstm_dims; 84 85 /** CMSIS-NN object for the per-channel quantization parameters */ 86 typedef struct 87 { 88 int32_t *multiplier; /**< Multiplier values */ 89 int32_t *shift; /**< Shift values */ 90 } cmsis_nn_per_channel_quant_params; 91 92 /** CMSIS-NN object for the per-tensor quantization parameters */ 93 typedef struct 94 { 95 int32_t multiplier; /**< Multiplier value */ 96 int32_t shift; /**< Shift value */ 97 } cmsis_nn_per_tensor_quant_params; 98 99 /** CMSIS-NN object for the quantized Relu activation */ 100 typedef struct 101 { 102 int32_t min; /**< Min value used to clamp the result */ 103 int32_t max; /**< Max value used to clamp the result */ 104 } cmsis_nn_activation; 105 106 /** CMSIS-NN object for the convolution layer parameters */ 107 typedef struct 108 { 109 int32_t input_offset; /**< Zero value for the input tensor */ 110 int32_t output_offset; /**< Zero value for the output tensor */ 111 cmsis_nn_tile stride; 112 cmsis_nn_tile padding; 113 cmsis_nn_tile dilation; 114 cmsis_nn_activation activation; 115 } cmsis_nn_conv_params; 116 117 /** CMSIS-NN object for Depthwise convolution layer parameters */ 118 typedef struct 119 { 120 int32_t input_offset; /**< Zero value for the input tensor */ 121 int32_t output_offset; /**< Zero value for the output tensor */ 122 int32_t ch_mult; /**< Channel Multiplier. ch_mult * in_ch = out_ch */ 123 cmsis_nn_tile stride; 124 cmsis_nn_tile padding; 125 cmsis_nn_tile dilation; 126 cmsis_nn_activation activation; 127 } cmsis_nn_dw_conv_params; 128 /** CMSIS-NN object for pooling layer parameters */ 129 typedef struct 130 { 131 cmsis_nn_tile stride; 132 cmsis_nn_tile padding; 133 cmsis_nn_activation activation; 134 } cmsis_nn_pool_params; 135 136 /** CMSIS-NN object for Fully Connected layer parameters */ 137 typedef struct 138 { 139 int32_t input_offset; /**< Zero value for the input tensor */ 140 int32_t filter_offset; /**< Zero value for the filter tensor. Not used */ 141 int32_t output_offset; /**< Zero value for the output tensor */ 142 cmsis_nn_activation activation; 143 } cmsis_nn_fc_params; 144 145 /** CMSIS-NN object for SVDF layer parameters */ 146 typedef struct 147 { 148 int32_t rank; 149 int32_t input_offset; /**< Zero value for the input tensor */ 150 int32_t output_offset; /**< Zero value for the output tensor */ 151 cmsis_nn_activation input_activation; 152 cmsis_nn_activation output_activation; 153 } cmsis_nn_svdf_params; 154 155 /** CMSIS-NN object for Softmax s16 layer parameters */ 156 typedef struct 157 { 158 const int16_t *exp_lut; 159 const int16_t *one_by_one_lut; 160 } cmsis_nn_softmax_lut_s16; 161 162 /** LSTM guard parameters */ 163 typedef struct 164 { 165 int32_t input_variance; 166 int32_t forget_variance; 167 int32_t cell_variance; 168 int32_t output_variance; 169 } cmsis_nn_lstm_guard_params; 170 171 /** LSTM scratch buffer container */ 172 typedef struct 173 { 174 int16_t *input_gate; 175 int16_t *forget_gate; 176 int16_t *cell_gate; 177 int16_t *output_gate; 178 } cmsis_nn_lstm_context; 179 180 /** Quantized clip value for cell and projection of LSTM input. Zero value means no clipping. */ 181 typedef struct 182 { 183 int16_t cell; 184 int8_t projection; 185 } cmsis_nn_lstm_clip_params; 186 187 /** CMSIS-NN object for quantization parameters */ 188 typedef struct 189 { 190 int32_t multiplier; /**< Multiplier value */ 191 int32_t shift; /**< Shift value */ 192 } cmsis_nn_scaling; 193 194 /** CMSIS-NN norm layer coefficients */ 195 typedef struct 196 { 197 int16_t *input_weight; 198 int16_t *forget_weight; 199 int16_t *cell_weight; 200 int16_t *output_weight; 201 } cmsis_nn_layer_norm; 202 203 /** Parameters for integer LSTM, as defined in TFLM */ 204 typedef struct 205 { 206 int32_t time_major; /**< Nonzero (true) if first row of data is timestamps for input */ 207 cmsis_nn_scaling input_to_input_scaling; 208 cmsis_nn_scaling input_to_forget_scaling; 209 cmsis_nn_scaling input_to_cell_scaling; 210 cmsis_nn_scaling input_to_output_scaling; 211 cmsis_nn_scaling recurrent_to_input_scaling; 212 cmsis_nn_scaling recurrent_to_forget_scaling; 213 cmsis_nn_scaling recurrent_to_cell_scaling; 214 cmsis_nn_scaling recurrent_to_output_scaling; 215 cmsis_nn_scaling cell_to_input_scaling; 216 cmsis_nn_scaling cell_to_forget_scaling; 217 cmsis_nn_scaling cell_to_output_scaling; 218 cmsis_nn_scaling projection_scaling; 219 cmsis_nn_scaling hidden_scaling; 220 cmsis_nn_scaling layer_norm_input_scaling; /**< layer normalization for input layer */ 221 cmsis_nn_scaling layer_norm_forget_scaling; /**< layer normalization for forget gate */ 222 cmsis_nn_scaling layer_norm_cell_scaling; /**< layer normalization for cell */ 223 cmsis_nn_scaling layer_norm_output_scaling; /**< layer normalization for outpus layer */ 224 225 int32_t cell_state_shift; 226 int32_t hidden_offset; 227 int32_t output_state_offset; 228 229 cmsis_nn_lstm_clip_params clip; 230 cmsis_nn_lstm_guard_params guard; 231 cmsis_nn_layer_norm layer_norm; 232 233 /* Effective bias is precalculated as bias + zero_point * weight. 234 Only applicable to when input/output are s8 and weights are s16 */ 235 const int32_t *i2i_effective_bias; /**< input to input effective bias */ 236 const int32_t *i2f_effective_bias; /**< input to forget gate effective bias */ 237 const int32_t *i2c_effective_bias; /**< input to cell effective bias */ 238 const int32_t *i2o_effective_bias; /**< input to output effective bias */ 239 240 const int32_t *r2i_effective_bias; /**< recurrent gate to input effective bias */ 241 const int32_t *r2f_effective_bias; /**< recurrent gate to forget gate effective bias */ 242 const int32_t *r2c_effective_bias; /**< recurrent gate to cell effective bias */ 243 const int32_t *r2o_effective_bias; /**< recurrent gate to output effective bias */ 244 245 const int32_t *projection_effective_bias; 246 247 /* Not precalculated bias */ 248 const int32_t *input_gate_bias; 249 const int32_t *forget_gate_bias; 250 const int32_t *cell_gate_bias; 251 const int32_t *output_gate_bias; 252 253 /* Activation min and max */ 254 cmsis_nn_activation activation; 255 256 } cmsis_nn_lstm_params; 257 258 #endif // _ARM_NN_TYPES_H 259
