Function arm_convolve_wrapper_s8¶
Defined in File arm_nnfunctions.h
Function Documentation¶
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arm_cmsis_nn_status arm_convolve_wrapper_s8(const cmsis_nn_context *ctx, const cmsis_nn_context *weight_sum_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)¶
s8 convolution layer wrapper function with the main purpose to call the optimal kernel available in cmsis-nn to perform the convolution.
- Parameters:
ctx – [inout] Function context that contains the additional buffer if required by the function. arm_convolve_wrapper_s8_get_buffer_size will return the buffer_size if required. The caller is expected to clear the buffer, if applicable, for security reasons.
weight_sum_ctx – [inout] Function context that contains the weight sum buffer if required by the function.
conv_params – [in] Convolution parameters (e.g. strides, dilations, pads,…). Range of conv_params->input_offset : [-127, 128] Range of conv_params->output_offset : [-128, 127]
quant_params – [in] Per-channel quantization info. It contains the multiplier and shift values to be applied to each output channel
input_dims – [in] Input (activation) tensor dimensions. Format: [N, H, W, C_IN]
input_data – [in] Input (activation) data pointer. Data type: int8
filter_dims – [in] Filter tensor dimensions. Format: [C_OUT, HK, WK, C_IN] where HK and WK are the spatial filter dimensions
filter_data – [in] Filter data pointer. Data type: int8
bias_dims – [in] Bias tensor dimensions. Format: [C_OUT]
bias_data – [in] Bias data pointer. Data type: int32
output_dims – [in] Output tensor dimensions. Format: [N, H, W, C_OUT]
output_data – [out] Output data pointer. Data type: int8
- Returns:
The function returns either
ARM_CMSIS_NN_ARG_ERRORif argument constraints fail. or,ARM_CMSIS_NN_SUCCESSon successful completion.