Function arm_convolve_s16

Function Documentation

arm_cmsis_nn_status arm_convolve_s16(const cmsis_nn_context *ctx, const cmsis_nn_conv_params *conv_params, const cmsis_nn_per_channel_quant_params *quant_params, const cmsis_nn_dims *input_dims, const int16_t *input_data, const cmsis_nn_dims *filter_dims, const int8_t *filter_data, const cmsis_nn_dims *bias_dims, const cmsis_nn_bias_data *bias_data, const cmsis_nn_dims *output_dims, int16_t *output_data)

Basic s16 convolution function.

  1. Supported framework: TensorFlow Lite micro

  2. Additional memory is required for optimization. Refer to argument ‘ctx’ for details.

Parameters:

  • ctx – [inout] Function context that contains the additional buffer if required by the function. arm_convolve_s16_get_buffer_size will return the buffer_size if required. The caller is expected to clear the buffer, if applicable, for security reasons.

  • conv_params – [in] Convolution parameters (e.g. strides, dilations, pads,…). conv_params->input_offset : Not used conv_params->output_offset : Not used

  • 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: int16

  • 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] Struct with optional bias data pointer. Bias data type can be int64 or int32 depending flag in struct.

  • output_dims – [in] Output tensor dimensions. Format: [N, H, W, C_OUT]

  • output_data – [out] Output data pointer. Data type: int16

Returns:

The function returns ARM_CMSIS_NN_SUCCESS if successful or ARM_CMSIS_NN_ARG_ERROR if incorrect arguments or ARM_CMSIS_NN_NO_IMPL_ERROR