/* * SPDX-FileCopyrightText: Copyright 2020-2024 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_nn_types.h * Description: Public header file to contain the CMSIS-NN structs for the * TensorFlowLite micro compliant functions * * $Date: 11 April 2024 * $Revision: V.3.2.0 * * Target : Arm(R) M-Profile Architecture * -------------------------------------------------------------------- */ #ifndef ARM_NN_TYPES_H #define ARM_NN_TYPES_H #include #include /** * @defgroup genPubTypes Structure Types * @ingroup Public * @brief Enums and Data Structures used in public API * @{ */ /** Enum for specifying activation function types */ typedef enum { ARM_SIGMOID = 0, /**< Sigmoid activation function */ ARM_TANH = 1, /**< Tanh activation function */ } arm_nn_activation_type; /** Function return codes */ typedef enum { ARM_CMSIS_NN_SUCCESS = 0, /**< No error */ ARM_CMSIS_NN_ARG_ERROR = -1, /**< One or more arguments are incorrect */ ARM_CMSIS_NN_NO_IMPL_ERROR = -2, /**< No implementation available */ ARM_CMSIS_NN_FAILURE = -3, /**< Logical error */ } arm_cmsis_nn_status; /** CMSIS-NN object to contain the width and height of a tile */ typedef struct { int32_t w; /**< Width */ int32_t h; /**< Height */ } cmsis_nn_tile; /** CMSIS-NN object used for the function context. */ typedef struct { void *buf; /**< Pointer to a buffer needed for the optimization */ int32_t size; /**< Buffer size */ } cmsis_nn_context; /** CMSIS-NN object used to hold bias data for int16 variants. */ typedef struct { const void *data; /**< Pointer to bias data */ const bool is_int32_bias; /**< Indicate type of bias data. True means int32 else int64 */ } cmsis_nn_bias_data; /** CMSIS-NN object to contain the dimensions of the tensors */ typedef struct { int32_t n; /**< Generic dimension to contain either the batch size or output channels. Please refer to the function documentation for more information */ int32_t h; /**< Height */ int32_t w; /**< Width */ int32_t c; /**< Input channels */ } cmsis_nn_dims; /** CMSIS-NN object to contain LSTM specific input parameters related to dimensions */ typedef struct { int32_t max_time; int32_t num_inputs; int32_t num_batches; int32_t num_outputs; } cmsis_nn_lstm_dims; /** CMSIS-NN object for the per-channel quantization parameters */ typedef struct { int32_t *multiplier; /**< Multiplier values */ int32_t *shift; /**< Shift values */ } cmsis_nn_per_channel_quant_params; /** CMSIS-NN object for the per-tensor quantization parameters */ typedef struct { int32_t multiplier; /**< Multiplier value */ int32_t shift; /**< Shift value */ } cmsis_nn_per_tensor_quant_params; /** CMSIS-NN object for the quantized Relu activation */ typedef struct { int32_t min; /**< Min value used to clamp the result */ int32_t max; /**< Max value used to clamp the result */ } cmsis_nn_activation; /** CMSIS-NN object for the convolution layer parameters */ typedef struct { int32_t input_offset; /**< The negative of the zero value for the input tensor */ int32_t output_offset; /**< The negative of the zero value for the output tensor */ cmsis_nn_tile stride; cmsis_nn_tile padding; cmsis_nn_tile dilation; cmsis_nn_activation activation; } cmsis_nn_conv_params; /** CMSIS-NN object for the transpose convolution layer parameters */ typedef struct { int32_t input_offset; /**< The negative of the zero value for the input tensor */ int32_t output_offset; /**< The negative of the zero value for the output tensor */ cmsis_nn_tile stride; cmsis_nn_tile padding; cmsis_nn_tile padding_offsets; cmsis_nn_tile dilation; cmsis_nn_activation activation; } cmsis_nn_transpose_conv_params; /** CMSIS-NN object for the depthwise convolution layer parameters */ typedef struct { int32_t input_offset; /**< The negative of the zero value for the input tensor */ int32_t output_offset; /**< The negative of the zero value for the output tensor */ int32_t ch_mult; /**< Channel Multiplier. ch_mult * in_ch = out_ch */ cmsis_nn_tile stride; cmsis_nn_tile padding; cmsis_nn_tile dilation; cmsis_nn_activation activation; } cmsis_nn_dw_conv_params; /** CMSIS-NN object for pooling layer parameters */ typedef struct { cmsis_nn_tile stride; cmsis_nn_tile padding; cmsis_nn_activation activation; } cmsis_nn_pool_params; /** CMSIS-NN object for Fully Connected layer parameters */ typedef struct { int32_t input_offset; /**< The negative of the zero value for the input tensor */ int32_t filter_offset; /**< The negative of the zero value for the filter tensor. Not used */ int32_t output_offset; /**< The negative of the zero value for the output tensor */ cmsis_nn_activation activation; } cmsis_nn_fc_params; /** CMSIS-NN object for SVDF layer parameters */ typedef struct { int32_t rank; int32_t input_offset; /**< The negative of the zero value for the input tensor */ int32_t output_offset; /**< The negative of the zero value for the output tensor */ cmsis_nn_activation input_activation; cmsis_nn_activation output_activation; } cmsis_nn_svdf_params; /** CMSIS-NN object for Softmax s16 layer parameters */ typedef struct { const int16_t *exp_lut; const int16_t *one_by_one_lut; } cmsis_nn_softmax_lut_s16; /** CMSIS-NN object for quantization parameters */ typedef struct { int32_t multiplier; /**< Multiplier value */ int32_t shift; /**< Shift value */ } cmsis_nn_scaling; /** CMSIS-NN object for LSTM gate parameters*/ typedef struct { int32_t input_multiplier; int32_t input_shift; const void *input_weights; const void *input_effective_bias; /**< Bias added with precomputed kernel_sum * lhs_offset*/ int32_t hidden_multiplier; int32_t hidden_shift; const void *hidden_weights; const void *hidden_effective_bias; /**< Precomputed kernel_sum * lhs_offset*/ const void *bias; arm_nn_activation_type activation_type; } cmsis_nn_lstm_gate; /** CMSIS-NN object for LSTM parameters*/ typedef struct { int32_t time_major; /**< 0 if first dimension is batch, else first dimension is time */ int32_t batch_size; int32_t time_steps; int32_t input_size; /**< Size of new data input into the LSTM cell*/ int32_t hidden_size; /**< Size of output from the LSTM cell, used as output and recursively into the next time step*/ int32_t input_offset; int32_t forget_to_cell_multiplier; int32_t forget_to_cell_shift; int32_t input_to_cell_multiplier; int32_t input_to_cell_shift; int32_t cell_clip; /**< Min/max value of cell output*/ int32_t cell_scale_power; int32_t output_multiplier; int32_t output_shift; int32_t output_offset; cmsis_nn_lstm_gate forget_gate; cmsis_nn_lstm_gate input_gate; cmsis_nn_lstm_gate cell_gate; cmsis_nn_lstm_gate output_gate; } cmsis_nn_lstm_params; /** CMSIS-NN object for LSTM scratch buffers*/ typedef struct { void *temp1; void *temp2; void *cell_state; } cmsis_nn_lstm_context; /** * @} // end group genPubTypes */ #endif /* ARM_NN_TYPES_H */