Function arm_convolve_1_x_n_s8

Function Documentation

arm_cmsis_nn_status arm_convolve_1_x_n_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)

1xn convolution

  • Supported framework : TensorFlow Lite Micro

  • The following constrains on the arguments apply

    1. input_dims->n equals 1

    2. ouput_dims->w is a multiple of 4

    3. Explicit constraints(since it is for 1xN convolution) -## input_dims->h equals 1 -## output_dims->h equals 1 -## filter_dims->h equals 1

      Todo:

      Remove constraint on output_dims->w to make the function generic.

Parameters:

  • ctx – [inout] Function context that contains the additional buffer if required by the function. arm_convolve_1_x_n_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 – [in] Per-output-channel weight sums, supplied by the caller. This function only reads the buffer and never writes it, so it is filled once and may then be reused for as long as filter_data, bias_data and conv_params->input_offset are unchanged - see arm_convolve_weight_sum() for the layout and the full reuse rules. Fill it with arm_convolve_weight_sum(), passing conv_params->input_offset as lhs_offset and the same bias_data given here. That helper returns ARM_CMSIS_NN_NO_IMPL_ERROR on non-MVE builds, which is not a failure. Pass a valid context on every build. Currently the contents are read only on builds with the MVE extension (ARM_MATH_MVEI), where an unfilled buffer yields wrong output while still returning ARM_CMSIS_NN_SUCCESS. A NULL buf is not checked for here, so do not rely on getting an error back. None of this is a guarantee about future versions. Sized by arm_convolve_s8_get_weights_sum_size(): output_dims->c * sizeof(int32_t) where the sums are used, 0 otherwise. The caller is expected to clear the buffer, if applicable, for security reasons.

  • 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, 1, WK, C_IN] where WK is the horizontal spatial filter dimension

  • filter_data – [in] Filter data pointer. Data type: int8

  • bias_dims – [in] Bias tensor dimensions. Format: [C_OUT]

  • bias_data – [in] Optional 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_ERROR if argument constraints fail. or, ARM_CMSIS_NN_SUCCESS on successful completion.