Convolution Functions

Collection of convolution, depthwise convolution functions and their variants. More...

Modules
	GetBufferSizeNNConv

Functions
riscv_nmsis_nn_status	riscv_convolve_1_x_n_s4 (const nmsis_nn_context *ctx, const nmsis_nn_conv_params *conv_params, const nmsis_nn_per_channel_quant_params *quant_params, const nmsis_nn_dims *input_dims, const int8_t *input_data, const nmsis_nn_dims *filter_dims, const int8_t *filter_data, const nmsis_nn_dims *bias_dims, const int32_t *bias_data, const nmsis_nn_dims *output_dims, int8_t *output_data)
	1xn convolution for s4 weights More...
riscv_nmsis_nn_status	riscv_convolve_1_x_n_s8 (const nmsis_nn_context *ctx, const nmsis_nn_conv_params *conv_params, const nmsis_nn_per_channel_quant_params *quant_params, const nmsis_nn_dims *input_dims, const int8_t *input_data, const nmsis_nn_dims *filter_dims, const int8_t *filter_data, const nmsis_nn_dims *bias_dims, const int32_t *bias_data, const nmsis_nn_dims *output_dims, int8_t *output_data)
	1xn convolution More...
riscv_nmsis_nn_status	riscv_convolve_1x1_HWC_q7_fast_nonsquare (const q7_t *Im_in, const uint16_t dim_im_in_x, const uint16_t dim_im_in_y, const uint16_t ch_im_in, const q7_t *wt, const uint16_t ch_im_out, const uint16_t dim_kernel_x, const uint16_t dim_kernel_y, const uint16_t padding_x, const uint16_t padding_y, const uint16_t stride_x, const uint16_t stride_y, const q7_t *bias, const uint16_t bias_shift, const uint16_t out_shift, q7_t *Im_out, const uint16_t dim_im_out_x, const uint16_t dim_im_out_y, q15_t *bufferA, q7_t *bufferB)
	Fast Q7 version of 1x1 convolution (non-sqaure shape) More...
riscv_nmsis_nn_status	riscv_convolve_1x1_s4 (const nmsis_nn_context *ctx, const nmsis_nn_conv_params *conv_params, const nmsis_nn_per_channel_quant_params *quant_params, const nmsis_nn_dims *input_dims, const int8_t *input_data, const nmsis_nn_dims *filter_dims, const int8_t *filter_data, const nmsis_nn_dims *bias_dims, const int32_t *bias_data, const nmsis_nn_dims *output_dims, int8_t *output_data)
	s4 version for 1x1 convolution with support for non-unity stride values More...
riscv_nmsis_nn_status	riscv_convolve_1x1_s4_fast (const nmsis_nn_context *ctx, const nmsis_nn_conv_params *conv_params, const nmsis_nn_per_channel_quant_params *quant_params, const nmsis_nn_dims *input_dims, const int8_t *input_data, const nmsis_nn_dims *filter_dims, const int8_t *filter_data, const nmsis_nn_dims *bias_dims, const int32_t *bias_data, const nmsis_nn_dims *output_dims, int8_t *output_data)
	Fast s4 version for 1x1 convolution (non-square shape) More...
riscv_nmsis_nn_status	riscv_convolve_1x1_s8 (const nmsis_nn_context *ctx, const nmsis_nn_conv_params *conv_params, const nmsis_nn_per_channel_quant_params *quant_params, const nmsis_nn_dims *input_dims, const int8_t *input_data, const nmsis_nn_dims *filter_dims, const int8_t *filter_data, const nmsis_nn_dims *bias_dims, const int32_t *bias_data, const nmsis_nn_dims *output_dims, int8_t *output_data)
	s8 version for 1x1 convolution with support for non-unity stride values More...
riscv_nmsis_nn_status	riscv_convolve_1x1_s8_fast (const nmsis_nn_context *ctx, const nmsis_nn_conv_params *conv_params, const nmsis_nn_per_channel_quant_params *quant_params, const nmsis_nn_dims *input_dims, const int8_t *input_data, const nmsis_nn_dims *filter_dims, const int8_t *filter_data, const nmsis_nn_dims *bias_dims, const int32_t *bias_data, const nmsis_nn_dims *output_dims, int8_t *output_data)
	Fast s8 version for 1x1 convolution (non-square shape) More...
riscv_nmsis_nn_status	riscv_convolve_even_s4 (const nmsis_nn_context *ctx, const nmsis_nn_conv_params *conv_params, const nmsis_nn_per_channel_quant_params *quant_params, const nmsis_nn_dims *input_dims, const int8_t *input_data, const nmsis_nn_dims *filter_dims, const int8_t *packed_filter_data, const nmsis_nn_dims *bias_dims, const int32_t *bias_data, const nmsis_nn_dims *output_dims, int8_t *output_data)
riscv_nmsis_nn_status	riscv_convolve_HWC_q15_basic (const q15_t *Im_in, const uint16_t dim_im_in, const uint16_t ch_im_in, const q15_t *wt, const uint16_t ch_im_out, const uint16_t dim_kernel, const uint16_t padding, const uint16_t stride, const q15_t *bias, const uint16_t bias_shift, const uint16_t out_shift, q15_t *Im_out, const uint16_t dim_im_out, q15_t *bufferA, q7_t *bufferB)
	Basic Q15 convolution function. More...
riscv_nmsis_nn_status	riscv_convolve_HWC_q15_fast (const q15_t *Im_in, const uint16_t dim_im_in, const uint16_t ch_im_in, const q15_t *wt, const uint16_t ch_im_out, const uint16_t dim_kernel, const uint16_t padding, const uint16_t stride, const q15_t *bias, const uint16_t bias_shift, const uint16_t out_shift, q15_t *Im_out, const uint16_t dim_im_out, q15_t *bufferA, q7_t *bufferB)
	Fast Q15 convolution function. More...
riscv_nmsis_nn_status	riscv_convolve_HWC_q15_fast_nonsquare (const q15_t *Im_in, const uint16_t dim_im_in_x, const uint16_t dim_im_in_y, const uint16_t ch_im_in, const q15_t *wt, const uint16_t ch_im_out, const uint16_t dim_kernel_x, const uint16_t dim_kernel_y, const uint16_t padding_x, const uint16_t padding_y, const uint16_t stride_x, const uint16_t stride_y, const q15_t *bias, const uint16_t bias_shift, const uint16_t out_shift, q15_t *Im_out, const uint16_t dim_im_out_x, const uint16_t dim_im_out_y, q15_t *bufferA, q7_t *bufferB)
	Fast Q15 convolution function (non-sqaure shape) More...
riscv_nmsis_nn_status	riscv_convolve_HWC_q7_basic (const q7_t *Im_in, const uint16_t dim_im_in, const uint16_t ch_im_in, const q7_t *wt, const uint16_t ch_im_out, const uint16_t dim_kernel, const uint16_t padding, const uint16_t stride, const q7_t *bias, const uint16_t bias_shift, const uint16_t out_shift, q7_t *Im_out, const uint16_t dim_im_out, q15_t *bufferA, q7_t *bufferB)
	Basic Q7 convolution function. More...
riscv_nmsis_nn_status	riscv_convolve_HWC_q7_basic_nonsquare (const q7_t *Im_in, const uint16_t dim_im_in_x, const uint16_t dim_im_in_y, const uint16_t ch_im_in, const q7_t *wt, const uint16_t ch_im_out, const uint16_t dim_kernel_x, const uint16_t dim_kernel_y, const uint16_t padding_x, const uint16_t padding_y, const uint16_t stride_x, const uint16_t stride_y, const q7_t *bias, const uint16_t bias_shift, const uint16_t out_shift, q7_t *Im_out, const uint16_t dim_im_out_x, const uint16_t dim_im_out_y, q15_t *bufferA, q7_t *bufferB)
	Basic Q7 convolution function (non-sqaure shape) More...
riscv_nmsis_nn_status	riscv_convolve_HWC_q7_fast (const q7_t *Im_in, const uint16_t dim_im_in, const uint16_t ch_im_in, const q7_t *wt, const uint16_t ch_im_out, const uint16_t dim_kernel, const uint16_t padding, const uint16_t stride, const q7_t *bias, const uint16_t bias_shift, const uint16_t out_shift, q7_t *Im_out, const uint16_t dim_im_out, q15_t *bufferA, q7_t *bufferB)
	Fast Q7 convolution function. More...
riscv_nmsis_nn_status	riscv_convolve_HWC_q7_fast_nonsquare (const q7_t *Im_in, const uint16_t dim_im_in_x, const uint16_t dim_im_in_y, const uint16_t ch_im_in, const q7_t *wt, const uint16_t ch_im_out, const uint16_t dim_kernel_x, const uint16_t dim_kernel_y, const uint16_t padding_x, const uint16_t padding_y, const uint16_t stride_x, const uint16_t stride_y, const q7_t *bias, const uint16_t bias_shift, const uint16_t out_shift, q7_t *Im_out, const uint16_t dim_im_out_x, const uint16_t dim_im_out_y, q15_t *bufferA, q7_t *bufferB)
	Fast Q7 convolution function (non-sqaure shape) More...
riscv_nmsis_nn_status	riscv_convolve_HWC_q7_RGB (const q7_t *Im_in, const uint16_t dim_im_in, const uint16_t ch_im_in, const q7_t *wt, const uint16_t ch_im_out, const uint16_t dim_kernel, const uint16_t padding, const uint16_t stride, const q7_t *bias, const uint16_t bias_shift, const uint16_t out_shift, q7_t *Im_out, const uint16_t dim_im_out, q15_t *bufferA, q7_t *bufferB)
	Q7 convolution function for RGB image. More...
riscv_nmsis_nn_status	riscv_convolve_s16 (const nmsis_nn_context *ctx, const nmsis_nn_conv_params *conv_params, const nmsis_nn_per_channel_quant_params *quant_params, const nmsis_nn_dims *input_dims, const int16_t *input_data, const nmsis_nn_dims *filter_dims, const int8_t *filter_data, const nmsis_nn_dims *bias_dims, const nmsis_nn_bias_data *bias_data, const nmsis_nn_dims *output_dims, int16_t *output_data)
	Basic s16 convolution function. More...
riscv_nmsis_nn_status	riscv_convolve_s4 (const nmsis_nn_context *ctx, const nmsis_nn_conv_params *conv_params, const nmsis_nn_per_channel_quant_params *quant_params, const nmsis_nn_dims *input_dims, const int8_t *input_data, const nmsis_nn_dims *filter_dims, const int8_t *packed_filter_data, const nmsis_nn_dims *bias_dims, const int32_t *bias_data, const nmsis_nn_dims *output_dims, int8_t *output_data)
	Basic s4 convolution function. More...
riscv_nmsis_nn_status	riscv_convolve_s8 (const nmsis_nn_context *ctx, const nmsis_nn_conv_params *conv_params, const nmsis_nn_per_channel_quant_params *quant_params, const nmsis_nn_dims *input_dims, const int8_t *input_data, const nmsis_nn_dims *filter_dims, const int8_t *filter_data, const nmsis_nn_dims *bias_dims, const int32_t *bias_data, const nmsis_nn_dims *upscale_dims, const nmsis_nn_dims *output_dims, int8_t *output_data)
	Basic s8 convolution function. More...
riscv_nmsis_nn_status	riscv_convolve_wrapper_s16 (const nmsis_nn_context *ctx, const nmsis_nn_conv_params *conv_params, const nmsis_nn_per_channel_quant_params *quant_params, const nmsis_nn_dims *input_dims, const int16_t *input_data, const nmsis_nn_dims *filter_dims, const int8_t *filter_data, const nmsis_nn_dims *bias_dims, const nmsis_nn_bias_data *bias_data, const nmsis_nn_dims *output_dims, int16_t *output_data)
	s16 convolution layer wrapper function with the main purpose to call the optimal kernel available in nmsis-nn to perform the convolution. More...
riscv_nmsis_nn_status	riscv_convolve_wrapper_s4 (const nmsis_nn_context *ctx, const nmsis_nn_conv_params *conv_params, const nmsis_nn_per_channel_quant_params *quant_params, const nmsis_nn_dims *input_dims, const int8_t *input_data, const nmsis_nn_dims *filter_dims, const int8_t *filter_data, const nmsis_nn_dims *bias_dims, const int32_t *bias_data, const nmsis_nn_dims *output_dims, int8_t *output_data)
	s4 convolution layer wrapper function with the main purpose to call the optimal kernel available in nmsis-nn to perform the convolution. More...
riscv_nmsis_nn_status	riscv_convolve_wrapper_s8 (const nmsis_nn_context *ctx, const nmsis_nn_conv_params *conv_params, const nmsis_nn_per_channel_quant_params *quant_params, const nmsis_nn_dims *input_dims, const int8_t *input_data, const nmsis_nn_dims *filter_dims, const int8_t *filter_data, const nmsis_nn_dims *bias_dims, const int32_t *bias_data, const nmsis_nn_dims *output_dims, int8_t *output_data)
	s8 convolution layer wrapper function with the main purpose to call the optimal kernel available in nmsis-nn to perform the convolution. More...
riscv_nmsis_nn_status	riscv_depthwise_conv_3x3_s8 (const nmsis_nn_context *ctx, const nmsis_nn_dw_conv_params *dw_conv_params, const nmsis_nn_per_channel_quant_params *quant_params, const nmsis_nn_dims *input_dims, const int8_t *input, const nmsis_nn_dims *filter_dims, const int8_t *kernel, const nmsis_nn_dims *bias_dims, const int32_t *bias, const nmsis_nn_dims *output_dims, int8_t *output)
	Optimized s8 depthwise convolution function for 3x3 kernel size with some constraints on the input arguments(documented below). Refer riscv_depthwise_conv_s8() for function argument details. More...
riscv_nmsis_nn_status	riscv_depthwise_conv_fast_s16 (const nmsis_nn_context *ctx, const nmsis_nn_dw_conv_params *dw_conv_params, const nmsis_nn_per_channel_quant_params *quant_params, const nmsis_nn_dims *input_dims, const int16_t *input, const nmsis_nn_dims *filter_dims, const int8_t *kernel, const nmsis_nn_dims *bias_dims, const int64_t *bias, const nmsis_nn_dims *output_dims, int16_t *output)
	Optimized s16 depthwise convolution function with constraint that in_channel equals out_channel. Refer riscv_depthwise_conv_s16() for function argument details. More...
static void	__attribute__ ((unused))
static void	depthwise_conv_s16_generic_s16 (const int16_t *input, const uint16_t input_batches, const uint16_t input_x, const uint16_t input_y, const uint16_t input_ch, const int8_t *kernel, const uint16_t ch_mult, const uint16_t kernel_x, const uint16_t kernel_y, const uint16_t pad_x, const uint16_t pad_y, const uint16_t stride_x, const uint16_t stride_y, const int64_t *bias, int16_t *output, const int32_t *output_shift, const int32_t *output_mult, const uint16_t output_x, const uint16_t output_y, const int32_t output_activation_min, const int32_t output_activation_max, const uint16_t dilation_x, const uint16_t dilation_y)
riscv_nmsis_nn_status	riscv_depthwise_conv_s16 (const nmsis_nn_context *ctx, const nmsis_nn_dw_conv_params *dw_conv_params, const nmsis_nn_per_channel_quant_params *quant_params, const nmsis_nn_dims *input_dims, const int16_t *input, const nmsis_nn_dims *filter_dims, const int8_t *kernel, const nmsis_nn_dims *bias_dims, const int64_t *bias, const nmsis_nn_dims *output_dims, int16_t *output)
	Basic s16 depthwise convolution function that doesn't have any constraints on the input dimensions. More...
static void	depthwise_conv_s4_generic (const int8_t *input, const int32_t input_batches, const int32_t input_x, const int32_t input_y, const int32_t input_ch, const int8_t *kernel, const int32_t output_ch, const int32_t ch_mult, const int32_t kernel_x, const int32_t kernel_y, const int32_t pad_x, const int32_t pad_y, const int32_t stride_x, const int32_t stride_y, const int32_t *bias, int8_t *output, const int32_t *output_shift, const int32_t *output_mult, const int32_t output_x, const int32_t output_y, const int32_t output_offset, const int32_t input_offset, const int32_t output_activation_min, const int32_t output_activation_max, const int32_t dilation_x, const int32_t dilation_y)
riscv_nmsis_nn_status	riscv_depthwise_conv_s4 (const nmsis_nn_context *ctx, const nmsis_nn_dw_conv_params *dw_conv_params, const nmsis_nn_per_channel_quant_params *quant_params, const nmsis_nn_dims *input_dims, const int8_t *input, const nmsis_nn_dims *filter_dims, const int8_t *kernel, const nmsis_nn_dims *bias_dims, const int32_t *bias, const nmsis_nn_dims *output_dims, int8_t *output)
	Basic s4 depthwise convolution function that doesn't have any constraints on the input dimensions. More...
riscv_nmsis_nn_status	riscv_depthwise_conv_s4_opt (const nmsis_nn_context *ctx, const nmsis_nn_dw_conv_params *dw_conv_params, const nmsis_nn_per_channel_quant_params *quant_params, const nmsis_nn_dims *input_dims, const int8_t *input, const nmsis_nn_dims *filter_dims, const int8_t *kernel, const nmsis_nn_dims *bias_dims, const int32_t *bias, const nmsis_nn_dims *output_dims, int8_t *output)
	Optimized s4 depthwise convolution function with constraint that in_channel equals out_channel. Refer riscv_depthwise_conv_s4() for function argument details. More...
static void	depthwise_conv_s8_mult_4 (const int8_t *input, const int32_t input_x, const int32_t input_y, const int32_t input_ch, const int8_t *kernel, const int32_t output_ch, const int32_t ch_mult, const int32_t kernel_x, const int32_t kernel_y, const int32_t pad_x, const int32_t pad_y, const int32_t stride_x, const int32_t stride_y, const int32_t *bias, int8_t *output, const int32_t *output_shift, const int32_t *output_mult, const int32_t output_x, const int32_t output_y, const int32_t output_offset, const int32_t input_offset, const int32_t output_activation_min, const int32_t output_activation_max)
static void	depthwise_conv_s8_generic (const int8_t *input, const uint16_t input_batches, const uint16_t input_x, const uint16_t input_y, const uint16_t input_ch, const int8_t *kernel, const uint16_t output_ch, const uint16_t ch_mult, const uint16_t kernel_x, const uint16_t kernel_y, const uint16_t pad_x, const uint16_t pad_y, const uint16_t stride_x, const uint16_t stride_y, const int32_t *bias, int8_t *output, const int32_t *output_shift, const int32_t *output_mult, const uint16_t output_x, const uint16_t output_y, const int32_t output_offset, const int32_t input_offset, const int32_t output_activation_min, const int32_t output_activation_max, const uint16_t dilation_x, const uint16_t dilation_y)
riscv_nmsis_nn_status	riscv_depthwise_conv_s8 (const nmsis_nn_context *ctx, const nmsis_nn_dw_conv_params *dw_conv_params, const nmsis_nn_per_channel_quant_params *quant_params, const nmsis_nn_dims *input_dims, const int8_t *input, const nmsis_nn_dims *filter_dims, const int8_t *kernel, const nmsis_nn_dims *bias_dims, const int32_t *bias, const nmsis_nn_dims *output_dims, int8_t *output)
	Basic s8 depthwise convolution function that doesn't have any constraints on the input dimensions. More...
riscv_nmsis_nn_status	riscv_depthwise_conv_s8_opt (const nmsis_nn_context *ctx, const nmsis_nn_dw_conv_params *dw_conv_params, const nmsis_nn_per_channel_quant_params *quant_params, const nmsis_nn_dims *input_dims, const int8_t *input, const nmsis_nn_dims *filter_dims, const int8_t *kernel, const nmsis_nn_dims *bias_dims, const int32_t *bias, const nmsis_nn_dims *output_dims, int8_t *output)
	Optimized s8 depthwise convolution function with constraint that in_channel equals out_channel. Refer riscv_depthwise_conv_s8() for function argument details. More...
riscv_nmsis_nn_status	riscv_depthwise_conv_wrapper_s16 (const nmsis_nn_context *ctx, const nmsis_nn_dw_conv_params *dw_conv_params, const nmsis_nn_per_channel_quant_params *quant_params, const nmsis_nn_dims *input_dims, const int16_t *input, const nmsis_nn_dims *filter_dims, const int8_t *filter, const nmsis_nn_dims *bias_dims, const int64_t *bias, const nmsis_nn_dims *output_dims, int16_t *output)
	Wrapper function to pick the right optimized s16 depthwise convolution function. More...
riscv_nmsis_nn_status	riscv_depthwise_conv_wrapper_s4 (const nmsis_nn_context *ctx, const nmsis_nn_dw_conv_params *dw_conv_params, const nmsis_nn_per_channel_quant_params *quant_params, const nmsis_nn_dims *input_dims, const int8_t *input, const nmsis_nn_dims *filter_dims, const int8_t *filter, const nmsis_nn_dims *bias_dims, const int32_t *bias, const nmsis_nn_dims *output_dims, int8_t *output)
	Wrapper function to pick the right optimized s4 depthwise convolution function. More...
riscv_nmsis_nn_status	riscv_depthwise_conv_wrapper_s8 (const nmsis_nn_context *ctx, const nmsis_nn_dw_conv_params *dw_conv_params, const nmsis_nn_per_channel_quant_params *quant_params, const nmsis_nn_dims *input_dims, const int8_t *input, const nmsis_nn_dims *filter_dims, const int8_t *filter, const nmsis_nn_dims *bias_dims, const int32_t *bias, const nmsis_nn_dims *output_dims, int8_t *output)
	Wrapper function to pick the right optimized s8 depthwise convolution function. More...
riscv_nmsis_nn_status	riscv_depthwise_separable_conv_HWC_q7 (const q7_t *Im_in, const uint16_t dim_im_in, const uint16_t ch_im_in, const q7_t *wt, const uint16_t ch_im_out, const uint16_t dim_kernel, const uint16_t padding, const uint16_t stride, const q7_t *bias, const uint16_t bias_shift, const uint16_t out_shift, q7_t *Im_out, const uint16_t dim_im_out, q15_t *bufferA, q7_t *bufferB)
	Q7 depthwise separable convolution function. More...
riscv_nmsis_nn_status	riscv_depthwise_separable_conv_HWC_q7_nonsquare (const q7_t *Im_in, const uint16_t dim_im_in_x, const uint16_t dim_im_in_y, const uint16_t ch_im_in, const q7_t *wt, const uint16_t ch_im_out, const uint16_t dim_kernel_x, const uint16_t dim_kernel_y, const uint16_t padding_x, const uint16_t padding_y, const uint16_t stride_x, const uint16_t stride_y, const q7_t *bias, const uint16_t bias_shift, const uint16_t out_shift, q7_t *Im_out, const uint16_t dim_im_out_x, const uint16_t dim_im_out_y, q15_t *bufferA, q7_t *bufferB)
	Q7 depthwise separable convolution function (non-square shape) More...
riscv_nmsis_nn_status	riscv_transpose_conv_s8 (const nmsis_nn_context *ctx, const nmsis_nn_context *output_ctx, const nmsis_nn_transpose_conv_params *transpose_conv_params, const nmsis_nn_per_channel_quant_params *quant_params, const nmsis_nn_dims *input_dims, const int8_t *input_data, const nmsis_nn_dims *filter_dims, const int8_t *filter_data, const nmsis_nn_dims *bias_dims, const int32_t *bias_data, const nmsis_nn_dims *output_dims, int8_t *output_data)
	Basic s8 transpose convolution function. More...
riscv_nmsis_nn_status	riscv_transpose_conv_wrapper_s8 (const nmsis_nn_context *ctx, const nmsis_nn_context *reverse_conv_ctx, const nmsis_nn_transpose_conv_params *transpose_conv_params, const nmsis_nn_per_channel_quant_params *quant_params, const nmsis_nn_dims *input_dims, const int8_t *input_data, const nmsis_nn_dims *filter_dims, const int8_t *filter_data, const nmsis_nn_dims *bias_dims, const int32_t *bias_data, const nmsis_nn_dims *output_dims, int8_t *output_data)
	Wrapper to select optimal transposed convolution algorithm depending on parameters. More...

Detailed Description

Collection of convolution, depthwise convolution functions and their variants.

The convolution is implemented in 2 steps: im2col and General Matrix Multiplication(GEMM)

im2col is a process of converting each patch of image data into a column. After im2col, the convolution is computed as matrix-matrix multiplication.

To reduce the memory footprint, the im2col is performed partially. Each iteration, only a few column (i.e., patches) are generated followed by GEMM.

Function Documentation

__attribute__()

static void __attribute__ ( (unused)  )

static

depthwise_conv_s16_generic_s16()

static void depthwise_conv_s16_generic_s16 ( const int16_t *  input, const uint16_t  input_batches, const uint16_t  input_x, const uint16_t  input_y, const uint16_t  input_ch, const int8_t *  kernel, const uint16_t  ch_mult, const uint16_t  kernel_x, const uint16_t  kernel_y, const uint16_t  pad_x, const uint16_t  pad_y, const uint16_t  stride_x, const uint16_t  stride_y, const int64_t *  bias, int16_t *  output, const int32_t *  output_shift, const int32_t *  output_mult, const uint16_t  output_x, const uint16_t  output_y, const int32_t  output_activation_min, const int32_t  output_activation_max, const uint16_t  dilation_x, const uint16_t  dilation_y  )

static

depthwise_conv_s4_generic()

static void depthwise_conv_s4_generic ( const int8_t *  input, const int32_t  input_batches, const int32_t  input_x, const int32_t  input_y, const int32_t  input_ch, const int8_t *  kernel, const int32_t  output_ch, const int32_t  ch_mult, const int32_t  kernel_x, const int32_t  kernel_y, const int32_t  pad_x, const int32_t  pad_y, const int32_t  stride_x, const int32_t  stride_y, const int32_t *  bias, int8_t *  output, const int32_t *  output_shift, const int32_t *  output_mult, const int32_t  output_x, const int32_t  output_y, const int32_t  output_offset, const int32_t  input_offset, const int32_t  output_activation_min, const int32_t  output_activation_max, const int32_t  dilation_x, const int32_t  dilation_y  )

static

depthwise_conv_s8_generic()

static void depthwise_conv_s8_generic ( const int8_t *  input, const uint16_t  input_batches, const uint16_t  input_x, const uint16_t  input_y, const uint16_t  input_ch, const int8_t *  kernel, const uint16_t  output_ch, const uint16_t  ch_mult, const uint16_t  kernel_x, const uint16_t  kernel_y, const uint16_t  pad_x, const uint16_t  pad_y, const uint16_t  stride_x, const uint16_t  stride_y, const int32_t *  bias, int8_t *  output, const int32_t *  output_shift, const int32_t *  output_mult, const uint16_t  output_x, const uint16_t  output_y, const int32_t  output_offset, const int32_t  input_offset, const int32_t  output_activation_min, const int32_t  output_activation_max, const uint16_t  dilation_x, const uint16_t  dilation_y  )

static

depthwise_conv_s8_mult_4()

static void depthwise_conv_s8_mult_4 ( const int8_t *  input, const int32_t  input_x, const int32_t  input_y, const int32_t  input_ch, const int8_t *  kernel, const int32_t  output_ch, const int32_t  ch_mult, const int32_t  kernel_x, const int32_t  kernel_y, const int32_t  pad_x, const int32_t  pad_y, const int32_t  stride_x, const int32_t  stride_y, const int32_t *  bias, int8_t *  output, const int32_t *  output_shift, const int32_t *  output_mult, const int32_t  output_x, const int32_t  output_y, const int32_t  output_offset, const int32_t  input_offset, const int32_t  output_activation_min, const int32_t  output_activation_max  )

static

riscv_convolve_1_x_n_s4()

riscv_nmsis_nn_status riscv_convolve_1_x_n_s4	(	const nmsis_nn_context *	ctx,
		const nmsis_nn_conv_params *	conv_params,
		const nmsis_nn_per_channel_quant_params *	quant_params,
		const nmsis_nn_dims *	input_dims,
		const int8_t *	input_data,
		const nmsis_nn_dims *	filter_dims,
		const int8_t *	filter_data,
		const nmsis_nn_dims *	bias_dims,
		const int32_t *	bias_data,
		const nmsis_nn_dims *	output_dims,
		int8_t *	output_data
	)

1xn convolution for s4 weights

Parameters

[in,out]	ctx	Function context that contains the additional buffer if required by the function. riscv_convolve_1_x_n_s4_get_buffer_size will return the buffer_size if required The caller is expected to clear the buffer, if applicable, for security reasons.
[in]	conv_params	Convolution parameters (e.g. strides, dilations, pads,...). Range of conv_params->input_offset : [-127, 128] Range of conv_params->output_offset : [-128, 127]
[in]	quant_params	Per-channel quantization info. It contains the multiplier and shift values to be applied to each output channel
[in]	input_dims	Input (activation) tensor dimensions. Format: [N, H, W, C_IN]
[in]	input_data	Input (activation) data pointer. Data type: int8
[in]	filter_dims	Filter tensor dimensions. Format: [C_OUT, 1, WK, C_IN] where WK is the horizontal spatial filter dimension
[in]	filter_data	Filter data pointer. Data type: int8 as packed int4
[in]	bias_dims	Bias tensor dimensions. Format: [C_OUT]
[in]	bias_data	Optional bias data pointer. Data type: int32
[in]	output_dims	Output tensor dimensions. Format: [N, H, W, C_OUT]
[out]	output_data	Output data pointer. Data type: int8

Returns

The function returns either RISCV_NMSIS_NN_ARG_ERROR if argument constraints fail. or, RISCV_NMSIS_NN_SUCCESS on successful completion.

• Supported framework : TensorFlow Lite Micro
• The following constrains on the arguments apply
  1. stride.w * input_dims->c is a multiple of 4
  2. 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.

riscv_convolve_1_x_n_s8()

riscv_nmsis_nn_status riscv_convolve_1_x_n_s8	(	const nmsis_nn_context *	ctx,
		const nmsis_nn_conv_params *	conv_params,
		const nmsis_nn_per_channel_quant_params *	quant_params,
		const nmsis_nn_dims *	input_dims,
		const int8_t *	input_data,
		const nmsis_nn_dims *	filter_dims,
		const int8_t *	filter_data,
		const nmsis_nn_dims *	bias_dims,
		const int32_t *	bias_data,
		const nmsis_nn_dims *	output_dims,
		int8_t *	output_data
	)

1xn convolution

Parameters

[in,out]	ctx	Function context that contains the additional buffer if required by the function. riscv_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.
[in]	conv_params	Convolution parameters (e.g. strides, dilations, pads,...). Range of conv_params->input_offset : [-127, 128] Range of conv_params->output_offset : [-128, 127]
[in]	quant_params	Per-channel quantization info. It contains the multiplier and shift values to be applied to each output channel
[in]	input_dims	Input (activation) tensor dimensions. Format: [N, H, W, C_IN]
[in]	input_data	Input (activation) data pointer. Data type: int8
[in]	filter_dims	Filter tensor dimensions. Format: [C_OUT, 1, WK, C_IN] where WK is the horizontal spatial filter dimension
[in]	filter_data	Filter data pointer. Data type: int8
[in]	bias_dims	Bias tensor dimensions. Format: [C_OUT]
[in]	bias_data	Optional bias data pointer. Data type: int32
[in]	output_dims	Output tensor dimensions. Format: [N, H, W, C_OUT]
[out]	output_data	Output data pointer. Data type: int8

Returns

The function returns either RISCV_NMSIS_NN_ARG_ERROR if argument constraints fail. or, RISCV_NMSIS_NN_SUCCESS on successful completion.

• 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.

riscv_convolve_1x1_HWC_q7_fast_nonsquare()

riscv_nmsis_nn_status riscv_convolve_1x1_HWC_q7_fast_nonsquare	(	const q7_t *	Im_in,
		const uint16_t	dim_im_in_x,
		const uint16_t	dim_im_in_y,
		const uint16_t	ch_im_in,
		const q7_t *	wt,
		const uint16_t	ch_im_out,
		const uint16_t	dim_kernel_x,
		const uint16_t	dim_kernel_y,
		const uint16_t	padding_x,
		const uint16_t	padding_y,
		const uint16_t	stride_x,
		const uint16_t	stride_y,
		const q7_t *	bias,
		const uint16_t	bias_shift,
		const uint16_t	out_shift,
		q7_t *	Im_out,
		const uint16_t	dim_im_out_x,
		const uint16_t	dim_im_out_y,
		q15_t *	bufferA,
		q7_t *	bufferB
	)

Fast Q7 version of 1x1 convolution (non-sqaure shape)

Parameters

[in]	Im_in	pointer to input tensor
[in]	dim_im_in_x	input tensor dimention x
[in]	dim_im_in_y	input tensor dimention y
[in]	ch_im_in	number of input tensor channels
[in]	wt	pointer to kernel weights
[in]	ch_im_out	number of filters, i.e., output tensor channels
[in]	dim_kernel_x	filter kernel size x
[in]	dim_kernel_y	filter kernel size y
[in]	padding_x	padding size x
[in]	padding_y	padding size y
[in]	stride_x	convolution stride x
[in]	stride_y	convolution stride y
[in]	bias	pointer to bias
[in]	bias_shift	amount of left-shift for bias
[in]	out_shift	amount of right-shift for output
[in,out]	Im_out	pointer to output tensor
[in]	dim_im_out_x	output tensor dimension x
[in]	dim_im_out_y	output tensor dimension y
[in,out]	bufferA	pointer to buffer space for input
[in,out]	bufferB	pointer to buffer space for output

Returns

The function returns either RISCV_NMSIS_NN_SIZE_MISMATCH or RISCV_NMSIS_NN_SUCCESS based on the outcome of size checking.

This function is optimized for convolution with 1x1 kernel size (i.e., dim_kernel_x=1 and dim_kernel_y=1). It can be used for the second half of MobileNets [1] after depthwise separable convolution.

This function is the version with full list of optimization tricks, but with some constraints: ch_im_in is multiple of 4 ch_im_out is multiple of 2

[1] MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications https://arxiv.org/abs/1704.04861

riscv_convolve_1x1_s4()

riscv_nmsis_nn_status riscv_convolve_1x1_s4	(	const nmsis_nn_context *	ctx,
		const nmsis_nn_conv_params *	conv_params,
		const nmsis_nn_per_channel_quant_params *	quant_params,
		const nmsis_nn_dims *	input_dims,
		const int8_t *	input_data,
		const nmsis_nn_dims *	filter_dims,
		const int8_t *	filter_data,
		const nmsis_nn_dims *	bias_dims,
		const int32_t *	bias_data,
		const nmsis_nn_dims *	output_dims,
		int8_t *	output_data
	)

s4 version for 1x1 convolution with support for non-unity stride values

Parameters

[in,out]	ctx	Function context that contains the additional buffer if required by the function. None is required by this function.
[in]	conv_params	Convolution parameters (e.g. strides, dilations, pads,...). Range of conv_params->input_offset : [-127, 128] Range of conv_params->output_offset : [-128, 127]
[in]	quant_params	Per-channel quantization info. It contains the multiplier and shift values to be applied to each output channel
[in]	input_dims	Input (activation) tensor dimensions. Format: [N, H, W, C_IN]
[in]	input_data	Input (activation) data pointer. Data type: int8
[in]	filter_dims	Filter tensor dimensions. Format: [C_OUT, 1, 1, C_IN]
[in]	filter_data	Filter data pointer. Data type: int8 packed with 2x int4
[in]	bias_dims	Bias tensor dimensions. Format: [C_OUT]
[in]	bias_data	Optional bias data pointer. Data type: int32
[in]	output_dims	Output tensor dimensions. Format: [N, H, W, C_OUT]
[out]	output_data	Output data pointer. Data type: int8

Returns

The function returns either RISCV_NMSIS_NN_ARG_ERROR if argument constraints fail. or, RISCV_NMSIS_NN_SUCCESS on successful completion.

• Supported framework : TensorFlow Lite Micro
• The following constrains on the arguments apply
  1. conv_params->padding.w = conv_params->padding.h = 0

riscv_convolve_1x1_s4_fast()

riscv_nmsis_nn_status riscv_convolve_1x1_s4_fast	(	const nmsis_nn_context *	ctx,
		const nmsis_nn_conv_params *	conv_params,
		const nmsis_nn_per_channel_quant_params *	quant_params,
		const nmsis_nn_dims *	input_dims,
		const int8_t *	input_data,
		const nmsis_nn_dims *	filter_dims,
		const int8_t *	filter_data,
		const nmsis_nn_dims *	bias_dims,
		const int32_t *	bias_data,
		const nmsis_nn_dims *	output_dims,
		int8_t *	output_data
	)

Fast s4 version for 1x1 convolution (non-square shape)

Parameters

[in,out]	ctx	Function context that contains the additional buffer if required by the function. riscv_convolve_1x1_s4_fast_get_buffer_size will return the buffer_size if required. The caller is expected to clear the buffer ,if applicable, for security reasons.
[in]	conv_params	Convolution parameters (e.g. strides, dilations, pads,...). Range of conv_params->input_offset : [-127, 128] Range of conv_params->output_offset : [-128, 127]
[in]	quant_params	Per-channel quantization info. It contains the multiplier and shift values to be applied to each output channel
[in]	input_dims	Input (activation) tensor dimensions. Format: [N, H, W, C_IN]
[in]	input_data	Input (activation) data pointer. Data type: int8
[in]	filter_dims	Filter tensor dimensions. Format: [C_OUT, 1, 1, C_IN]
[in]	filter_data	Filter data pointer. Data type: int8 packed with 2x int4
[in]	bias_dims	Bias tensor dimensions. Format: [C_OUT]
[in]	bias_data	Optional bias data pointer. Data type: int32
[in]	output_dims	Output tensor dimensions. Format: [N, H, W, C_OUT]
[out]	output_data	Output data pointer. Data type: int8

Returns

The function returns either RISCV_NMSIS_NN_ARG_ERROR if argument constraints fail. or, RISCV_NMSIS_NN_SUCCESS on successful completion.

• Supported framework : TensorFlow Lite Micro
• The following constrains on the arguments apply
  1. conv_params->padding.w = conv_params->padding.h = 0
  2. conv_params->stride.w = conv_params->stride.h = 1

riscv_convolve_1x1_s8()

riscv_nmsis_nn_status riscv_convolve_1x1_s8	(	const nmsis_nn_context *	ctx,
		const nmsis_nn_conv_params *	conv_params,
		const nmsis_nn_per_channel_quant_params *	quant_params,
		const nmsis_nn_dims *	input_dims,
		const int8_t *	input_data,
		const nmsis_nn_dims *	filter_dims,
		const int8_t *	filter_data,
		const nmsis_nn_dims *	bias_dims,
		const int32_t *	bias_data,
		const nmsis_nn_dims *	output_dims,
		int8_t *	output_data
	)

s8 version for 1x1 convolution with support for non-unity stride values

Parameters

[in,out]	ctx	Function context that contains the additional buffer if required by the function. None is required by this function.
[in]	conv_params	Convolution parameters (e.g. strides, dilations, pads,...). Range of conv_params->input_offset : [-127, 128] Range of conv_params->output_offset : [-128, 127]
[in]	quant_params	Per-channel quantization info. It contains the multiplier and shift values to be applied to each output channel
[in]	input_dims	Input (activation) tensor dimensions. Format: [N, H, W, C_IN]
[in]	input_data	Input (activation) data pointer. Data type: int8
[in]	filter_dims	Filter tensor dimensions. Format: [C_OUT, 1, 1, C_IN]
[in]	filter_data	Filter data pointer. Data type: int8
[in]	bias_dims	Bias tensor dimensions. Format: [C_OUT]
[in]	bias_data	Optional bias data pointer. Data type: int32
[in]	output_dims	Output tensor dimensions. Format: [N, H, W, C_OUT]
[out]	output_data	Output data pointer. Data type: int8

Returns

The function returns either RISCV_NMSIS_NN_ARG_ERROR if argument constraints fail. or, RISCV_NMSIS_NN_SUCCESS on successful completion.

• Supported framework : TensorFlow Lite Micro
• The following constrains on the arguments apply
  1. conv_params->padding.w = conv_params->padding.h = 0

riscv_convolve_1x1_s8_fast()

riscv_nmsis_nn_status riscv_convolve_1x1_s8_fast	(	const nmsis_nn_context *	ctx,
		const nmsis_nn_conv_params *	conv_params,
		const nmsis_nn_per_channel_quant_params *	quant_params,
		const nmsis_nn_dims *	input_dims,
		const int8_t *	input_data,
		const nmsis_nn_dims *	filter_dims,
		const int8_t *	filter_data,
		const nmsis_nn_dims *	bias_dims,
		const int32_t *	bias_data,
		const nmsis_nn_dims *	output_dims,
		int8_t *	output_data
	)

Fast s8 version for 1x1 convolution (non-square shape)

Parameters

[in,out]	ctx	Function context that contains the additional buffer if required by the function. riscv_convolve_1x1_s8_fast_get_buffer_size will return the buffer_size if required. The caller is expected to clear the buffer, if applicable, for security reasons.
[in]	conv_params	Convolution parameters (e.g. strides, dilations, pads,...). Range of conv_params->input_offset : [-127, 128] Range of conv_params->output_offset : [-128, 127]
[in]	quant_params	Per-channel quantization info. It contains the multiplier and shift values to be applied to each output channel
[in]	input_dims	Input (activation) tensor dimensions. Format: [N, H, W, C_IN]
[in]	input_data	Input (activation) data pointer. Data type: int8
[in]	filter_dims	Filter tensor dimensions. Format: [C_OUT, 1, 1, C_IN]
[in]	filter_data	Filter data pointer. Data type: int8
[in]	bias_dims	Bias tensor dimensions. Format: [C_OUT]
[in]	bias_data	Optional bias data pointer. Data type: int32
[in]	output_dims	Output tensor dimensions. Format: [N, H, W, C_OUT]
[out]	output_data	Output data pointer. Data type: int8

Returns

The function returns either RISCV_NMSIS_NN_ARG_ERROR if argument constraints fail. or, RISCV_NMSIS_NN_SUCCESS on successful completion.

• Supported framework : TensorFlow Lite Micro
• The following constrains on the arguments apply
  1. conv_params->padding.w = conv_params->padding.h = 0
  2. conv_params->stride.w = conv_params->stride.h = 1

Parameters

[in,out]	ctx	Function context that contains the additional buffer if required by the function. riscv_convolve_1x1_s8_fast_get_buffer_size will return the buffer_size if required
[in]	conv_params	Convolution parameters (e.g. strides, dilations, pads,...). Range of conv_params->input_offset : [-127, 128] Range of conv_params->output_offset : [-128, 127]
[in]	quant_params	Per-channel quantization info. It contains the multiplier and shift values to be applied to each output channel
[in]	input_dims	Input (activation) tensor dimensions. Format: [N, H, W, C_IN]
[in]	input_data	Input (activation) data pointer. Data type: int8
[in]	filter_dims	Filter tensor dimensions. Format: [C_OUT, 1, 1, C_IN]
[in]	filter_data	Filter data pointer. Data type: int8
[in]	bias_dims	Bias tensor dimensions. Format: [C_OUT]
[in]	bias_data	Optional bias data pointer. Data type: int32
[in]	output_dims	Output tensor dimensions. Format: [N, H, W, C_OUT]
[out]	output_data	Output data pointer. Data type: int8

Returns

The function returns either RISCV_NMSIS_NN_SIZE_MISMATCH if argument constraints fail. or, RISCV_NMSIS_NN_SUCCESS on successful completion.

• Supported framework : TensorFlow Lite Micro
• The following constrains on the arguments apply
  1. input_dims->c is a multiple of 4
  2. conv_params->padding.w = conv_params->padding.h = 0
  3. conv_params->stride.w = conv_params->stride.h = 1

riscv_convolve_even_s4()

riscv_nmsis_nn_status riscv_convolve_even_s4	(	const nmsis_nn_context *	ctx,
		const nmsis_nn_conv_params *	conv_params,
		const nmsis_nn_per_channel_quant_params *	quant_params,
		const nmsis_nn_dims *	input_dims,
		const int8_t *	input_data,
		const nmsis_nn_dims *	filter_dims,
		const int8_t *	packed_filter_data,
		const nmsis_nn_dims *	bias_dims,
		const int32_t *	bias_data,
		const nmsis_nn_dims *	output_dims,
		int8_t *	output_data
	)

riscv_convolve_HWC_q15_basic()

riscv_nmsis_nn_status riscv_convolve_HWC_q15_basic	(	const q15_t *	Im_in,
		const uint16_t	dim_im_in,
		const uint16_t	ch_im_in,
		const q15_t *	wt,
		const uint16_t	ch_im_out,
		const uint16_t	dim_kernel,
		const uint16_t	padding,
		const uint16_t	stride,
		const q15_t *	bias,
		const uint16_t	bias_shift,
		const uint16_t	out_shift,
		q15_t *	Im_out,
		const uint16_t	dim_im_out,
		q15_t *	bufferA,
		q7_t *	bufferB
	)

Basic Q15 convolution function.

Parameters

[in]	Im_in	pointer to input tensor
[in]	dim_im_in	input tensor dimention
[in]	ch_im_in	number of input tensor channels
[in]	wt	pointer to kernel weights
[in]	ch_im_out	number of filters, i.e., output tensor channels
[in]	dim_kernel	filter kernel size
[in]	padding	padding sizes
[in]	stride	convolution stride
[in]	bias	pointer to bias
[in]	bias_shift	amount of left-shift for bias
[in]	out_shift	amount of right-shift for output
[in,out]	Im_out	pointer to output tensor
[in]	dim_im_out	output tensor dimension
[in,out]	bufferA	pointer to buffer space for input
[in,out]	bufferB	pointer to buffer space for output

Returns

The function returns RISCV_NMSIS_NN_SUCCESS

Buffer size:

bufferA size: ch_im_in*dim_kernel*dim_kernel

bufferB size: 0

This basic version is designed to work for any input tensor and weight dimension.

riscv_convolve_HWC_q15_fast()

riscv_nmsis_nn_status riscv_convolve_HWC_q15_fast	(	const q15_t *	Im_in,
		const uint16_t	dim_im_in,
		const uint16_t	ch_im_in,
		const q15_t *	wt,
		const uint16_t	ch_im_out,
		const uint16_t	dim_kernel,
		const uint16_t	padding,
		const uint16_t	stride,
		const q15_t *	bias,
		const uint16_t	bias_shift,
		const uint16_t	out_shift,
		q15_t *	Im_out,
		const uint16_t	dim_im_out,
		q15_t *	bufferA,
		q7_t *	bufferB
	)

Fast Q15 convolution function.

Parameters

[in]	Im_in	pointer to input tensor
[in]	dim_im_in	input tensor dimention
[in]	ch_im_in	number of input tensor channels
[in]	wt	pointer to kernel weights
[in]	ch_im_out	number of filters, i.e., output tensor channels
[in]	dim_kernel	filter kernel size
[in]	padding	padding sizes
[in]	stride	convolution stride
[in]	bias	pointer to bias
[in]	bias_shift	amount of left-shift for bias
[in]	out_shift	amount of right-shift for output
[in,out]	Im_out	pointer to output tensor
[in]	dim_im_out	output tensor dimension
[in,out]	bufferA	pointer to buffer space for input
[in,out]	bufferB	pointer to buffer space for output

Returns

The function returns either RISCV_NMSIS_NN_SIZE_MISMATCH or RISCV_NMSIS_NN_SUCCESS based on the outcome of size checking.

Buffer size:

bufferA size: 2*ch_im_in*dim_kernel*dim_kernel

bufferB size: 0

Input dimension constraints:

ch_im_in is multiple of 2

ch_im_out is multiple of 2

dim_im_out is a multiple of 2

riscv_convolve_HWC_q15_fast_nonsquare()

riscv_nmsis_nn_status riscv_convolve_HWC_q15_fast_nonsquare	(	const q15_t *	Im_in,
		const uint16_t	dim_im_in_x,
		const uint16_t	dim_im_in_y,
		const uint16_t	ch_im_in,
		const q15_t *	wt,
		const uint16_t	ch_im_out,
		const uint16_t	dim_kernel_x,
		const uint16_t	dim_kernel_y,
		const uint16_t	padding_x,
		const uint16_t	padding_y,
		const uint16_t	stride_x,
		const uint16_t	stride_y,
		const q15_t *	bias,
		const uint16_t	bias_shift,
		const uint16_t	out_shift,
		q15_t *	Im_out,
		const uint16_t	dim_im_out_x,
		const uint16_t	dim_im_out_y,
		q15_t *	bufferA,
		q7_t *	bufferB
	)

Fast Q15 convolution function (non-sqaure shape)

Parameters

[in]	Im_in	pointer to input tensor
[in]	dim_im_in_x	input tensor dimention x
[in]	dim_im_in_y	input tensor dimention y
[in]	ch_im_in	number of input tensor channels
[in]	wt	pointer to kernel weights
[in]	ch_im_out	number of filters, i.e., output tensor channels
[in]	dim_kernel_x	filter kernel size x
[in]	dim_kernel_y	filter kernel size y
[in]	padding_x	padding size x
[in]	padding_y	padding size y
[in]	stride_x	convolution stride x
[in]	stride_y	convolution stride y
[in]	bias	pointer to bias
[in]	bias_shift	amount of left-shift for bias
[in]	out_shift	amount of right-shift for output
[in,out]	Im_out	pointer to output tensor
[in]	dim_im_out_x	output tensor dimension x
[in]	dim_im_out_y	output tensor dimension y
[in,out]	bufferA	pointer to buffer space for input
[in,out]	bufferB	pointer to buffer space for output

Returns

The function returns either RISCV_NMSIS_NN_SIZE_MISMATCH or RISCV_NMSIS_NN_SUCCESS based on the outcome of size checking.

Buffer size:

bufferA size: 2*ch_im_in*dim_kernel*dim_kernel

bufferB size: 0

Input dimension constraints:

ch_im_in is multiple of 2

ch_im_out is multiple of 2

riscv_convolve_HWC_q7_basic()

riscv_nmsis_nn_status riscv_convolve_HWC_q7_basic	(	const q7_t *	Im_in,
		const uint16_t	dim_im_in,
		const uint16_t	ch_im_in,
		const q7_t *	wt,
		const uint16_t	ch_im_out,
		const uint16_t	dim_kernel,
		const uint16_t	padding,
		const uint16_t	stride,
		const q7_t *	bias,
		const uint16_t	bias_shift,
		const uint16_t	out_shift,
		q7_t *	Im_out,
		const uint16_t	dim_im_out,
		q15_t *	bufferA,
		q7_t *	bufferB
	)

Basic Q7 convolution function.

Parameters

[in]	Im_in	pointer to input tensor
[in]	dim_im_in	input tensor dimention
[in]	ch_im_in	number of input tensor channels
[in]	wt	pointer to kernel weights
[in]	ch_im_out	number of filters, i.e., output tensor channels
[in]	dim_kernel	filter kernel size
[in]	padding	padding sizes
[in]	stride	convolution stride
[in]	bias	pointer to bias
[in]	bias_shift	amount of left-shift for bias
[in]	out_shift	amount of right-shift for output
[in,out]	Im_out	pointer to output tensor
[in]	dim_im_out	output tensor dimension
[in,out]	bufferA	pointer to buffer space for input
[in,out]	bufferB	pointer to buffer space for output

Returns

The function returns RISCV_NMSIS_NN_SUCCESS

Buffer size:

bufferA size: 2*ch_im_in*dim_kernel*dim_kernel

bufferB size: 0

This basic version is designed to work for any input tensor and weight dimension.

riscv_convolve_HWC_q7_basic_nonsquare()

riscv_nmsis_nn_status riscv_convolve_HWC_q7_basic_nonsquare	(	const q7_t *	Im_in,
		const uint16_t	dim_im_in_x,
		const uint16_t	dim_im_in_y,
		const uint16_t	ch_im_in,
		const q7_t *	wt,
		const uint16_t	ch_im_out,
		const uint16_t	dim_kernel_x,
		const uint16_t	dim_kernel_y,
		const uint16_t	padding_x,
		const uint16_t	padding_y,
		const uint16_t	stride_x,
		const uint16_t	stride_y,
		const q7_t *	bias,
		const uint16_t	bias_shift,
		const uint16_t	out_shift,
		q7_t *	Im_out,
		const uint16_t	dim_im_out_x,
		const uint16_t	dim_im_out_y,
		q15_t *	bufferA,
		q7_t *	bufferB
	)

Basic Q7 convolution function (non-sqaure shape)

Basic Q7 convolution function (non-square shape)

Parameters

[in]	Im_in	pointer to input tensor
[in]	dim_im_in_x	input tensor dimention x
[in]	dim_im_in_y	input tensor dimention y
[in]	ch_im_in	number of input tensor channels
[in]	wt	pointer to kernel weights
[in]	ch_im_out	number of filters, i.e., output tensor channels
[in]	dim_kernel_x	filter kernel size x
[in]	dim_kernel_y	filter kernel size y
[in]	padding_x	padding size x
[in]	padding_y	padding size y
[in]	stride_x	convolution stride x
[in]	stride_y	convolution stride y
[in]	bias	pointer to bias
[in]	bias_shift	amount of left-shift for bias
[in]	out_shift	amount of right-shift for output
[in,out]	Im_out	pointer to output tensor
[in]	dim_im_out_x	output tensor dimension x
[in]	dim_im_out_y	output tensor dimension y
[in,out]	bufferA	pointer to buffer space for input
[in,out]	bufferB	pointer to buffer space for output

Returns

The function returns RISCV_NMSIS_NN_SUCCESS

riscv_convolve_HWC_q7_fast()

riscv_nmsis_nn_status riscv_convolve_HWC_q7_fast	(	const q7_t *	Im_in,
		const uint16_t	dim_im_in,
		const uint16_t	ch_im_in,
		const q7_t *	wt,
		const uint16_t	ch_im_out,
		const uint16_t	dim_kernel,
		const uint16_t	padding,
		const uint16_t	stride,
		const q7_t *	bias,
		const uint16_t	bias_shift,
		const uint16_t	out_shift,
		q7_t *	Im_out,
		const uint16_t	dim_im_out,
		q15_t *	bufferA,
		q7_t *	bufferB
	)

Fast Q7 convolution function.

Parameters

[in]	Im_in	pointer to input tensor
[in]	dim_im_in	input tensor dimention
[in]	ch_im_in	number of input tensor channels
[in]	wt	pointer to kernel weights
[in]	ch_im_out	number of filters, i.e., output tensor channels
[in]	dim_kernel	filter kernel size
[in]	padding	padding sizes
[in]	stride	convolution stride
[in]	bias	pointer to bias
[in]	bias_shift	amount of left-shift for bias
[in]	out_shift	amount of right-shift for output
[in,out]	Im_out	pointer to output tensor
[in]	dim_im_out	output tensor dimension
[in,out]	bufferA	pointer to buffer space for input
[in,out]	bufferB	pointer to buffer space for output

Returns

The function returns either RISCV_NMSIS_NN_SIZE_MISMATCH or RISCV_NMSIS_NN_SUCCESS based on the outcome of size checking.

Buffer size:

bufferA size: 2*ch_im_in*dim_kernel*dim_kernel

bufferB size: 0

Input dimension constraints:

ch_im_in is multiple of 4 ( because of the SIMD32 read and swap )

ch_im_out is multiple of 2 ( bacause 2x2 mat_mult kernel )

The im2col converts the Q7 tensor input into Q15 column, which is stored in bufferA. There is reordering happenning during this im2col process with riscv_q7_to_q15_reordered_no_shift. For every four elements, the second and third elements are swapped.

The computation kernel riscv_nn_mat_mult_kernel_q7_q15_reordered does the GEMM computation with the reordered columns.

To speed-up the determination of the padding condition, we split the computation into 3x3 parts, i.e., {top, mid, bottom} X {left, mid, right}. This reduces the total number of boundary condition checks and improves the data copying performance.

riscv_convolve_HWC_q7_fast_nonsquare()

riscv_nmsis_nn_status riscv_convolve_HWC_q7_fast_nonsquare	(	const q7_t *	Im_in,
		const uint16_t	dim_im_in_x,
		const uint16_t	dim_im_in_y,
		const uint16_t	ch_im_in,
		const q7_t *	wt,
		const uint16_t	ch_im_out,
		const uint16_t	dim_kernel_x,
		const uint16_t	dim_kernel_y,
		const uint16_t	padding_x,
		const uint16_t	padding_y,
		const uint16_t	stride_x,
		const uint16_t	stride_y,
		const q7_t *	bias,
		const uint16_t	bias_shift,
		const uint16_t	out_shift,
		q7_t *	Im_out,
		const uint16_t	dim_im_out_x,
		const uint16_t	dim_im_out_y,
		q15_t *	bufferA,
		q7_t *	bufferB
	)

Fast Q7 convolution function (non-sqaure shape)

Parameters

[in]	Im_in	pointer to input tensor
[in]	dim_im_in_x	input tensor dimention x
[in]	dim_im_in_y	input tensor dimention y
[in]	ch_im_in	number of input tensor channels
[in]	wt	pointer to kernel weights
[in]	ch_im_out	number of filters, i.e., output tensor channels
[in]	dim_kernel_x	filter kernel size x
[in]	dim_kernel_y	filter kernel size y
[in]	padding_x	padding size x
[in]	padding_y	padding size y
[in]	stride_x	convolution stride x
[in]	stride_y	convolution stride y
[in]	bias	pointer to bias
[in]	bias_shift	amount of left-shift for bias
[in]	out_shift	amount of right-shift for output
[in,out]	Im_out	pointer to output tensor
[in]	dim_im_out_x	output tensor dimension x
[in]	dim_im_out_y	output tensor dimension y
[in,out]	bufferA	pointer to buffer space for input
[in,out]	bufferB	pointer to buffer space for output

Returns

The function returns either RISCV_NMSIS_NN_SIZE_MISMATCH or RISCV_NMSIS_NN_SUCCESS based on the outcome of size checking.

This function is the version with full list of optimization tricks, but with some constraints: ch_im_in is multiple of 4 ch_im_out is multiple of 2

riscv_convolve_HWC_q7_RGB()

riscv_nmsis_nn_status riscv_convolve_HWC_q7_RGB	(	const q7_t *	Im_in,
		const uint16_t	dim_im_in,
		const uint16_t	ch_im_in,
		const q7_t *	wt,
		const uint16_t	ch_im_out,
		const uint16_t	dim_kernel,
		const uint16_t	padding,
		const uint16_t	stride,
		const q7_t *	bias,
		const uint16_t	bias_shift,
		const uint16_t	out_shift,
		q7_t *	Im_out,
		const uint16_t	dim_im_out,
		q15_t *	bufferA,
		q7_t *	bufferB
	)

Q7 convolution function for RGB image.

Q7 version of convolution for RGB image.

Parameters

[in]	Im_in	pointer to input tensor
[in]	dim_im_in	input tensor dimention
[in]	ch_im_in	number of input tensor channels
[in]	wt	pointer to kernel weights
[in]	ch_im_out	number of filters, i.e., output tensor channels
[in]	dim_kernel	filter kernel size
[in]	padding	padding sizes
[in]	stride	convolution stride
[in]	bias	pointer to bias
[in]	bias_shift	amount of left-shift for bias
[in]	out_shift	amount of right-shift for output
[in,out]	Im_out	pointer to output tensor
[in]	dim_im_out	output tensor dimension
[in,out]	bufferA	pointer to buffer space for input
[in,out]	bufferB	pointer to buffer space for output

Returns

The function returns either RISCV_NMSIS_NN_SIZE_MISMATCH or RISCV_NMSIS_NN_SUCCESS based on the outcome of size checking.

Buffer size:

bufferA size: 2*ch_im_in*dim_kernel*dim_kernel

bufferB size: 0

Input dimension constraints:

ch_im_in equals 3

This kernel is written exclusively for convolution with ch_im_in equals 3. This applies on the first layer of CNNs which has input image with RGB format.

riscv_convolve_s16()

riscv_nmsis_nn_status riscv_convolve_s16	(	const nmsis_nn_context *	ctx,
		const nmsis_nn_conv_params *	conv_params,
		const nmsis_nn_per_channel_quant_params *	quant_params,
		const nmsis_nn_dims *	input_dims,
		const int16_t *	input_data,
		const nmsis_nn_dims *	filter_dims,
		const int8_t *	filter_data,
		const nmsis_nn_dims *	bias_dims,
		const nmsis_nn_bias_data *	bias_data,
		const nmsis_nn_dims *	output_dims,
		int16_t *	output_data
	)

Basic s16 convolution function.

Parameters

[in,out]	ctx	Function context that contains the additional buffer if required by the function. riscv_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.
[in]	conv_params	Convolution parameters (e.g. strides, dilations, pads,...). conv_params->input_offset : Not used conv_params->output_offset : Not used
[in]	quant_params	Per-channel quantization info. It contains the multiplier and shift values to be applied to each output channel
[in]	input_dims	Input (activation) tensor dimensions. Format: [N, H, W, C_IN]
[in]	input_data	Input (activation) data pointer. Data type: int16
[in]	filter_dims	Filter tensor dimensions. Format: [C_OUT, HK, WK, C_IN] where HK and WK are the spatial filter dimensions
[in]	filter_data	Filter data pointer. Data type: int8
[in]	bias_dims	Bias tensor dimensions. Format: [C_OUT]
[in]	bias_data	Struct with optional bias data pointer. Bias data type can be int64 or int32 depending flag in struct.
[in]	output_dims	Output tensor dimensions. Format: [N, H, W, C_OUT]
[out]	output_data	Output data pointer. Data type: int16

Returns

The function returns RISCV_NMSIS_NN_SUCCESS if successful or RISCV_NMSIS_NN_ARG_ERROR if incorrect arguments or RISCV_NMSIS_NN_NO_IMPL_ERROR

1. Supported framework: TensorFlow Lite micro
2. Additional memory is required for optimization. Refer to argument 'ctx' for details.

riscv_convolve_s4()

riscv_nmsis_nn_status riscv_convolve_s4	(	const nmsis_nn_context *	ctx,
		const nmsis_nn_conv_params *	conv_params,
		const nmsis_nn_per_channel_quant_params *	quant_params,
		const nmsis_nn_dims *	input_dims,
		const int8_t *	input_data,
		const nmsis_nn_dims *	filter_dims,
		const int8_t *	filter_data,
		const nmsis_nn_dims *	bias_dims,
		const int32_t *	bias_data,
		const nmsis_nn_dims *	output_dims,
		int8_t *	output_data
	)

Basic s4 convolution function.

Parameters

[in,out]	ctx	Function context that contains the additional buffer if required by the function. riscv_convolve_s4_get_buffer_size will return the buffer_size if required. The caller is expected to clear the buffer ,if applicable, for security reasons.
[in]	conv_params	Convolution parameters (e.g. strides, dilations, pads,...). Range of conv_params->input_offset : [-127, 128] Range of conv_params->output_offset : [-128, 127]
[in]	quant_params	Per-channel quantization info. It contains the multiplier and shift values to be applied to each output channel
[in]	input_dims	Input (activation) tensor dimensions. Format: [N, H, W, C_IN]
[in]	input_data	Input (activation) data pointer. Data type: int8
[in]	filter_dims	Filter tensor dimensions. Format: [C_OUT, HK, WK, C_IN] where HK and WK are the spatial filter dimensions
[in]	filter_data	Packed Filter data pointer. Data type: int8 packed with 2x int4
[in]	bias_dims	Bias tensor dimensions. Format: [C_OUT]
[in]	bias_data	Optional bias data pointer. Data type: int32
[in]	output_dims	Output tensor dimensions. Format: [N, H, W, C_OUT]
[out]	output_data	Output data pointer. Data type: int8

Returns

The function returns RISCV_NMSIS_NN_SUCCESS

1. Supported framework: TensorFlow Lite micro
2. Additional memory is required for optimization. Refer to argument 'ctx' for details.

riscv_convolve_s8()

riscv_nmsis_nn_status riscv_convolve_s8	(	const nmsis_nn_context *	ctx,
		const nmsis_nn_conv_params *	conv_params,
		const nmsis_nn_per_channel_quant_params *	quant_params,
		const nmsis_nn_dims *	input_dims,
		const int8_t *	input_data,
		const nmsis_nn_dims *	filter_dims,
		const int8_t *	filter_data,
		const nmsis_nn_dims *	bias_dims,
		const int32_t *	bias_data,
		const nmsis_nn_dims *	upscale_dims,
		const nmsis_nn_dims *	output_dims,
		int8_t *	output_data
	)

Basic s8 convolution function.

Parameters

[in,out]	ctx	Function context that contains the additional buffer if required by the function. riscv_convolve_s8_get_buffer_size will return the buffer_size if required. The caller is expected to clear the buffer, if applicable, for security reasons.
[in]	conv_params	Convolution parameters (e.g. strides, dilations, pads,...). Range of conv_params->input_offset : [-127, 128] Range of conv_params->output_offset : [-128, 127]
[in]	quant_params	Per-channel quantization info. It contains the multiplier and shift values to be applied to each output channel
[in]	input_dims	Input (activation) tensor dimensions. Format: [N, H, W, C_IN]
[in]	input_data	Input (activation) data pointer. Data type: int8
[in]	filter_dims	Filter tensor dimensions. Format: [C_OUT, HK, WK, CK] where HK, WK and CK are the spatial filter dimensions. CK != C_IN is used for grouped convolution, in which case the required conditions are C_IN = N * CK and C_OUT = N * M for N groups of size M.
[in]	filter_data	Filter data pointer. Data type: int8
[in]	bias_dims	Bias tensor dimensions. Format: [C_OUT]
[in]	bias_data	Optional bias data pointer. Data type: int32
[in]	upscale_dims	Inserts zeroes to upscale the input in h/w dimensions if set to 2. This is used for tranposed convolution.
[in]	output_dims	Output tensor dimensions. Format: [N, H, W, C_OUT]
[out]	output_data	Output data pointer. Data type: int8

Returns

The function returns RISCV_NMSIS_NN_SUCCESS if successful or RISCV_NMSIS_NN_ARG_ERROR if incorrect arguments or RISCV_NMSIS_NN_NO_IMPL_ERROR

1. Supported framework: TensorFlow Lite micro
2. Additional memory is required for optimization. Refer to argument 'ctx' for details.

riscv_convolve_wrapper_s16()

riscv_nmsis_nn_status riscv_convolve_wrapper_s16	(	const nmsis_nn_context *	ctx,
		const nmsis_nn_conv_params *	conv_params,
		const nmsis_nn_per_channel_quant_params *	quant_params,
		const nmsis_nn_dims *	input_dims,
		const int16_t *	input_data,
		const nmsis_nn_dims *	filter_dims,
		const int8_t *	filter_data,
		const nmsis_nn_dims *	bias_dims,
		const nmsis_nn_bias_data *	bias_data,
		const nmsis_nn_dims *	output_dims,
		int16_t *	output_data
	)

s16 convolution layer wrapper function with the main purpose to call the optimal kernel available in nmsis-nn to perform the convolution.

Parameters

[in,out]	ctx	Function context that contains the additional buffer if required by the function. riscv_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.
[in]	conv_params	Convolution parameters (e.g. strides, dilations, pads,...). conv_params->input_offset : Not used conv_params->output_offset : Not used
[in]	quant_params	Per-channel quantization info. It contains the multiplier and shift values to be applied to each output channel
[in]	input_dims	Input (activation) tensor dimensions. Format: [N, H, W, C_IN]
[in]	input_data	Input (activation) data pointer. Data type: int16
[in]	filter_dims	Filter tensor dimensions. Format: [C_OUT, HK, WK, C_IN] where HK and WK are the spatial filter dimensions
[in]	filter_data	Filter data pointer. Data type: int8
[in]	bias_dims	Bias tensor dimensions. Format: [C_OUT]
[in]	bias_data	Struct with optional bias data pointer. Bias data type can be int64 or int32 depending flag in struct.
[in]	output_dims	Output tensor dimensions. Format: [N, H, W, C_OUT]
[out]	output_data	Output data pointer. Data type: int16

Returns

The function returns either RISCV_NMSIS_NN_ARG_ERROR if argument constraints fail. or, RISCV_NMSIS_NN_SUCCESS on successful completion.

riscv_convolve_wrapper_s4()

riscv_nmsis_nn_status riscv_convolve_wrapper_s4	(	const nmsis_nn_context *	ctx,
		const nmsis_nn_conv_params *	conv_params,
		const nmsis_nn_per_channel_quant_params *	quant_params,
		const nmsis_nn_dims *	input_dims,
		const int8_t *	input_data,
		const nmsis_nn_dims *	filter_dims,
		const int8_t *	filter_data,
		const nmsis_nn_dims *	bias_dims,
		const int32_t *	bias_data,
		const nmsis_nn_dims *	output_dims,
		int8_t *	output_data
	)

s4 convolution layer wrapper function with the main purpose to call the optimal kernel available in nmsis-nn to perform the convolution.

Parameters

[in,out]	ctx	Function context that contains the additional buffer if required by the function. riscv_convolve_wrapper_s4_get_buffer_size will return the buffer_size if required. The caller is expected to clear the buffer ,if applicable, for security reasons.
[in]	conv_params	Convolution parameters (e.g. strides, dilations, pads,...). Range of conv_params->input_offset : [-127, 128] Range of conv_params->output_offset : [-128, 127]
[in]	quant_params	Per-channel quantization info. It contains the multiplier and shift values to be applied to each output channel
[in]	input_dims	Input (activation) tensor dimensions. Format: [N, H, W, C_IN]
[in]	input_data	Input (activation) data pointer. Data type: int8
[in]	filter_dims	Filter tensor dimensions. Format: [C_OUT, HK, WK, C_IN] where HK and WK are the spatial filter dimensions
[in]	filter_data	Filter data pointer. Data type: int8 packed with 2x int4
[in]	bias_dims	Bias tensor dimensions. Format: [C_OUT]
[in]	bias_data	Bias data pointer. Data type: int32
[in]	output_dims	Output tensor dimensions. Format: [N, H, W, C_OUT]
[out]	output_data	Output data pointer. Data type: int8

Returns

The function returns either RISCV_NMSIS_NN_ARG_ERROR if argument constraints fail. or, RISCV_NMSIS_NN_SUCCESS on successful completion.

riscv_convolve_wrapper_s8()

riscv_nmsis_nn_status riscv_convolve_wrapper_s8	(	const nmsis_nn_context *	ctx,
		const nmsis_nn_conv_params *	conv_params,
		const nmsis_nn_per_channel_quant_params *	quant_params,
		const nmsis_nn_dims *	input_dims,
		const int8_t *	input_data,
		const nmsis_nn_dims *	filter_dims,
		const int8_t *	filter_data,
		const nmsis_nn_dims *	bias_dims,
		const int32_t *	bias_data,
		const nmsis_nn_dims *	output_dims,
		int8_t *	output_data
	)

s8 convolution layer wrapper function with the main purpose to call the optimal kernel available in nmsis-nn to perform the convolution.

Parameters

[in,out]	ctx	Function context that contains the additional buffer if required by the function. riscv_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.
[in]	conv_params	Convolution parameters (e.g. strides, dilations, pads,...). Range of conv_params->input_offset : [-127, 128] Range of conv_params->output_offset : [-128, 127]
[in]	quant_params	Per-channel quantization info. It contains the multiplier and shift values to be applied to each output channel
[in]	input_dims	Input (activation) tensor dimensions. Format: [N, H, W, C_IN]
[in]	input_data	Input (activation) data pointer. Data type: int8
[in]	filter_dims	Filter tensor dimensions. Format: [C_OUT, HK, WK, C_IN] where HK and WK are the spatial filter dimensions
[in]	filter_data	Filter data pointer. Data type: int8
[in]	bias_dims	Bias tensor dimensions. Format: [C_OUT]
[in]	bias_data	Bias data pointer. Data type: int32
[in]	output_dims	Output tensor dimensions. Format: [N, H, W, C_OUT]
[out]	output_data	Output data pointer. Data type: int8

Returns

The function returns either RISCV_NMSIS_NN_ARG_ERROR if argument constraints fail. or, RISCV_NMSIS_NN_SUCCESS on successful completion.

riscv_depthwise_conv_3x3_s8()

riscv_nmsis_nn_status riscv_depthwise_conv_3x3_s8	(	const nmsis_nn_context *	ctx,
		const nmsis_nn_dw_conv_params *	dw_conv_params,
		const nmsis_nn_per_channel_quant_params *	quant_params,
		const nmsis_nn_dims *	input_dims,
		const int8_t *	input_data,
		const nmsis_nn_dims *	filter_dims,
		const int8_t *	filter_data,
		const nmsis_nn_dims *	bias_dims,
		const int32_t *	bias_data,
		const nmsis_nn_dims *	output_dims,
		int8_t *	output_data
	)

Optimized s8 depthwise convolution function for 3x3 kernel size with some constraints on the input arguments(documented below). Refer riscv_depthwise_conv_s8() for function argument details.

Returns

The function returns one of the following RISCV_NMSIS_NN_ARG_ERROR - Unsupported dimension of tensors

• Unsupported pad size along the x axis RISCV_NMSIS_NN_SUCCESS - Successful operation

• Supported framework : TensorFlow Lite Micro
• The following constrains on the arguments apply
  1. Number of input channel equals number of output channels
  2. Filter height and width equals 3
  3. Padding along x is either 0 or 1.

riscv_depthwise_conv_fast_s16()

riscv_nmsis_nn_status riscv_depthwise_conv_fast_s16	(	const nmsis_nn_context *	ctx,
		const nmsis_nn_dw_conv_params *	dw_conv_params,
		const nmsis_nn_per_channel_quant_params *	quant_params,
		const nmsis_nn_dims *	input_dims,
		const int16_t *	input_data,
		const nmsis_nn_dims *	filter_dims,
		const int8_t *	filter_data,
		const nmsis_nn_dims *	bias_dims,
		const int64_t *	bias_data,
		const nmsis_nn_dims *	output_dims,
		int16_t *	output_data
	)

Optimized s16 depthwise convolution function with constraint that in_channel equals out_channel. Refer riscv_depthwise_conv_s16() for function argument details.

Returns

The function returns one of the following RISCV_NMSIS_NN_ARG_ERROR - ctx-buff == NULL and riscv_depthwise_conv_fast_s16_get_buffer_size() > 0 or input channel != output channel or ch_mult != 1

RISCV_NMSIS_NN_SUCCESS - Successful operation

• Supported framework: TensorFlow Lite
• The following constrains on the arguments apply
  1. Number of input channel equals number of output channels or ch_mult equals 1
• Reccomended when number of channels is 4 or greater.

riscv_depthwise_conv_s16()

riscv_nmsis_nn_status riscv_depthwise_conv_s16	(	const nmsis_nn_context *	ctx,
		const nmsis_nn_dw_conv_params *	dw_conv_params,
		const nmsis_nn_per_channel_quant_params *	quant_params,
		const nmsis_nn_dims *	input_dims,
		const int16_t *	input_data,
		const nmsis_nn_dims *	filter_dims,
		const int8_t *	filter_data,
		const nmsis_nn_dims *	bias_dims,
		const int64_t *	bias_data,
		const nmsis_nn_dims *	output_dims,
		int16_t *	output_data
	)

Basic s16 depthwise convolution function that doesn't have any constraints on the input dimensions.

Parameters

[in,out]	ctx	Function context (e.g. temporary buffer). Check the function definition file to see if an additional buffer is required. Optional function {API}_get_buffer_size() provides the buffer size if an additional buffer is required. exists if additional memory is. The caller is expected to clear the buffer, if applicable, for security reasons.
[in]	dw_conv_params	Depthwise convolution parameters (e.g. strides, dilations, pads,...) conv_params->input_offset : Not used conv_params->output_offset : Not used
[in]	quant_params	Per-channel quantization info. It contains the multiplier and shift values to be applied to each output channel
[in]	input_dims	Input (activation) tensor dimensions. Format: [N, H, W, C_IN] Batch argument N is not used.
[in]	input_data	Input (activation) data pointer. Data type: int8
[in]	filter_dims	Filter tensor dimensions. Format: [1, H, W, C_OUT]
[in]	filter_data	Filter data pointer. Data type: int8
[in]	bias_dims	Bias tensor dimensions. Format: [C_OUT]
[in]	bias_data	Bias data pointer. Data type: int64
[in]	output_dims	Output tensor dimensions. Format: [N, H, W, C_OUT]
[in,out]	output_data	Output data pointer. Data type: int16

Returns

The function returns RISCV_NMSIS_NN_SUCCESS

• Supported framework: TensorFlow Lite

riscv_depthwise_conv_s4()

riscv_nmsis_nn_status riscv_depthwise_conv_s4	(	const nmsis_nn_context *	ctx,
		const nmsis_nn_dw_conv_params *	dw_conv_params,
		const nmsis_nn_per_channel_quant_params *	quant_params,
		const nmsis_nn_dims *	input_dims,
		const int8_t *	input,
		const nmsis_nn_dims *	filter_dims,
		const int8_t *	kernel,
		const nmsis_nn_dims *	bias_dims,
		const int32_t *	bias,
		const nmsis_nn_dims *	output_dims,
		int8_t *	output
	)

Basic s4 depthwise convolution function that doesn't have any constraints on the input dimensions.

Parameters

[in,out]	ctx	Function context (e.g. temporary buffer). Check the function definition file to see if an additional buffer is required. Optional function {API}_get_buffer_size() provides the buffer size if an additional buffer is required exists if additional memory is. The caller is expected to clear the buffer ,if applicable, for security reasons.
[in]	dw_conv_params	Depthwise convolution parameters (e.g. strides, dilations, pads,...) dw_conv_params->dilation is not used. Range of dw_conv_params->input_offset : [-127, 128] Range of dw_conv_params->input_offset : [-128, 127]
[in]	quant_params	Per-channel quantization info. It contains the multiplier and shift values to be applied to each output channel
[in]	input_dims	Input (activation) tensor dimensions. Format: [N, H, W, C_IN] Batch argument N is not used.
[in]	input	Input (activation) data pointer. Data type: int8
[in]	filter_dims	Filter tensor dimensions. Format: [1, H, W, C_OUT]
[in]	kernel	Filter data pointer. Data type: int8_t packed 4-bit weights, e.g four sequential weights [0x1, 0x2, 0x3, 0x4] packed as [0x21, 0x43].
[in]	bias_dims	Bias tensor dimensions. Format: [C_OUT]
[in]	bias	Bias data pointer. Data type: int32
[in]	output_dims	Output tensor dimensions. Format: [N, H, W, C_OUT]
[in,out]	output	Output data pointer. Data type: int8

Returns

The function returns RISCV_NMSIS_NN_SUCCESS

• Supported framework: TensorFlow Lite

riscv_depthwise_conv_s4_opt()

riscv_nmsis_nn_status riscv_depthwise_conv_s4_opt	(	const nmsis_nn_context *	ctx,
		const nmsis_nn_dw_conv_params *	dw_conv_params,
		const nmsis_nn_per_channel_quant_params *	quant_params,
		const nmsis_nn_dims *	input_dims,
		const int8_t *	input_data,
		const nmsis_nn_dims *	filter_dims,
		const int8_t *	filter_data,
		const nmsis_nn_dims *	bias_dims,
		const int32_t *	bias_data,
		const nmsis_nn_dims *	output_dims,
		int8_t *	output_data
	)

Optimized s4 depthwise convolution function with constraint that in_channel equals out_channel. Refer riscv_depthwise_conv_s4() for function argument details.

Returns

The function returns one of the following RISCV_NMSIS_NN_ARG_ERROR - input channel != output channel or ch_mult != 1 RISCV_NMSIS_NN_SUCCESS - Successful operation

• Supported framework: TensorFlow Lite
• The following constrains on the arguments apply
  1. Number of input channel equals number of output channels or ch_mult equals 1
• Reccomended when number of channels is 4 or greater.

riscv_depthwise_conv_s8()

riscv_nmsis_nn_status riscv_depthwise_conv_s8	(	const nmsis_nn_context *	ctx,
		const nmsis_nn_dw_conv_params *	dw_conv_params,
		const nmsis_nn_per_channel_quant_params *	quant_params,
		const nmsis_nn_dims *	input_dims,
		const int8_t *	input_data,
		const nmsis_nn_dims *	filter_dims,
		const int8_t *	filter_data,
		const nmsis_nn_dims *	bias_dims,
		const int32_t *	bias_data,
		const nmsis_nn_dims *	output_dims,
		int8_t *	output_data
	)

Basic s8 depthwise convolution function that doesn't have any constraints on the input dimensions.

Parameters

[in,out]	ctx	Function context (e.g. temporary buffer). Check the function definition file to see if an additional buffer is required. Optional function {API}_get_buffer_size() provides the buffer size if an additional buffer is required exists if additional memory is. The caller is expected to clear the buffer, if applicable, for security reasons.
[in]	dw_conv_params	Depthwise convolution parameters (e.g. strides, dilations, pads,...) dw_conv_params->dilation is not used. Range of dw_conv_params->input_offset : [-127, 128] Range of dw_conv_params->input_offset : [-128, 127]
[in]	quant_params	Per-channel quantization info. It contains the multiplier and shift values to be applied to each output channel
[in]	input_dims	Input (activation) tensor dimensions. Format: [N, H, W, C_IN] Batch argument N is not used.
[in]	input_data	Input (activation) data pointer. Data type: int8
[in]	filter_dims	Filter tensor dimensions. Format: [1, H, W, C_OUT]
[in]	filter_data	Filter data pointer. Data type: int8
[in]	bias_dims	Bias tensor dimensions. Format: [C_OUT]
[in]	bias_data	Bias data pointer. Data type: int32
[in]	output_dims	Output tensor dimensions. Format: [N, H, W, C_OUT]
[in,out]	output_data	Output data pointer. Data type: int8

Returns

The function returns RISCV_NMSIS_NN_SUCCESS

• Supported framework: TensorFlow Lite

riscv_depthwise_conv_s8_opt()

riscv_nmsis_nn_status riscv_depthwise_conv_s8_opt	(	const nmsis_nn_context *	ctx,
		const nmsis_nn_dw_conv_params *	dw_conv_params,
		const nmsis_nn_per_channel_quant_params *	quant_params,
		const nmsis_nn_dims *	input_dims,
		const int8_t *	input_data,
		const nmsis_nn_dims *	filter_dims,
		const int8_t *	filter_data,
		const nmsis_nn_dims *	bias_dims,
		const int32_t *	bias_data,
		const nmsis_nn_dims *	output_dims,
		int8_t *	output_data
	)

Optimized s8 depthwise convolution function with constraint that in_channel equals out_channel. Refer riscv_depthwise_conv_s8() for function argument details.

Returns

The function returns one of the following RISCV_NMSIS_NN_ARG_ERROR - input channel != output channel or ch_mult != 1 RISCV_NMSIS_NN_SUCCESS - Successful operation

• Supported framework: TensorFlow Lite
• The following constrains on the arguments apply
  1. Number of input channel equals number of output channels or ch_mult equals 1
• Reccomended when number of channels is 4 or greater.

riscv_depthwise_conv_wrapper_s16()

riscv_nmsis_nn_status riscv_depthwise_conv_wrapper_s16	(	const nmsis_nn_context *	ctx,
		const nmsis_nn_dw_conv_params *	dw_conv_params,
		const nmsis_nn_per_channel_quant_params *	quant_params,
		const nmsis_nn_dims *	input_dims,
		const int16_t *	input_data,
		const nmsis_nn_dims *	filter_dims,
		const int8_t *	filter_data,
		const nmsis_nn_dims *	bias_dims,
		const int64_t *	bias_data,
		const nmsis_nn_dims *	output_dims,
		int16_t *	output_data
	)

Wrapper function to pick the right optimized s16 depthwise convolution function.

Parameters

[in,out]	ctx	Function context (e.g. temporary buffer). Check the function definition file to see if an additional buffer is required. Optional function {API}_get_buffer_size() provides the buffer size if required. The caller is expected to clear the buffer, if applicable, for security reasons.
[in]	dw_conv_params	Depthwise convolution parameters (e.g. strides, dilations, pads,...) dw_conv_params->dilation is not used. Range of dw_conv_params->input_offset : Not used Range of dw_conv_params->output_offset : Not used
[in]	quant_params	Per-channel quantization info. It contains the multiplier and shift values to be applied to each output channel
[in]	input_dims	Input (activation) tensor dimensions. Format: [H, W, C_IN] Batch argument N is not used and assumed to be 1.
[in]	input_data	Input (activation) data pointer. Data type: int16
[in]	filter_dims	Filter tensor dimensions. Format: [1, H, W, C_OUT]
[in]	filter_data	Filter data pointer. Data type: int8
[in]	bias_dims	Bias tensor dimensions. Format: [C_OUT]
[in]	bias_data	Bias data pointer. Data type: int64
[in]	output_dims	Output tensor dimensions. Format: [1, H, W, C_OUT]
[in,out]	output_data	Output data pointer. Data type: int16

Returns

The function returns RISCV_NMSIS_NN_SUCCESS - Successful completion.

• Supported framework: TensorFlow Lite
• Picks one of the the following functions
  1. riscv_depthwise_conv_s16()
  2. riscv_depthwise_conv_fast_s16() - RISC-V CPUs with DSP extension only

riscv_depthwise_conv_wrapper_s4()

riscv_nmsis_nn_status riscv_depthwise_conv_wrapper_s4	(	const nmsis_nn_context *	ctx,
		const nmsis_nn_dw_conv_params *	dw_conv_params,
		const nmsis_nn_per_channel_quant_params *	quant_params,
		const nmsis_nn_dims *	input_dims,
		const int8_t *	input_data,
		const nmsis_nn_dims *	filter_dims,
		const int8_t *	filter_data,
		const nmsis_nn_dims *	bias_dims,
		const int32_t *	bias_data,
		const nmsis_nn_dims *	output_dims,
		int8_t *	output_data
	)

Wrapper function to pick the right optimized s4 depthwise convolution function.

Parameters

[in,out]	ctx	Function context (e.g. temporary buffer). Check the function definition file to see if an additional buffer is required. Optional function {API}_get_buffer_size() provides the buffer size if required. The caller is expected to clear the buffer ,if applicable, for security reasons.
[in]	dw_conv_params	Depthwise convolution parameters (e.g. strides, dilations, pads,...) dw_conv_params->dilation is not used. Range of dw_conv_params->input_offset : [-127, 128] Range of dw_conv_params->output_offset : [-128, 127]
[in]	quant_params	Per-channel quantization info. It contains the multiplier and shift values to be applied to each output channel
[in]	input_dims	Input (activation) tensor dimensions. Format: [H, W, C_IN] Batch argument N is not used and assumed to be 1.
[in]	input_data	Input (activation) data pointer. Data type: int8
[in]	filter_dims	Filter tensor dimensions. Format: [1, H, W, C_OUT]
[in]	filter_data	Filter data pointer. Data type: int8_t packed 4-bit weights, e.g four sequential weights [0x1, 0x2, 0x3, 0x4] packed as [0x21, 0x43].
[in]	bias_dims	Bias tensor dimensions. Format: [C_OUT]
[in]	bias_data	Bias data pointer. Data type: int32
[in]	output_dims	Output tensor dimensions. Format: [1, H, W, C_OUT]
[in,out]	output_data	Output data pointer. Data type: int8

Returns

The function returns RISCV_NMSIS_NN_SUCCESS - Successful completion.

• Supported framework: TensorFlow Lite

riscv_depthwise_conv_wrapper_s8()

riscv_nmsis_nn_status riscv_depthwise_conv_wrapper_s8	(	const nmsis_nn_context *	ctx,
		const nmsis_nn_dw_conv_params *	dw_conv_params,
		const nmsis_nn_per_channel_quant_params *	quant_params,
		const nmsis_nn_dims *	input_dims,
		const int8_t *	input_data,
		const nmsis_nn_dims *	filter_dims,
		const int8_t *	filter_data,
		const nmsis_nn_dims *	bias_dims,
		const int32_t *	bias_data,
		const nmsis_nn_dims *	output_dims,
		int8_t *	output_data
	)

Wrapper function to pick the right optimized s8 depthwise convolution function.

Parameters

[in,out]	ctx	Function context (e.g. temporary buffer). Check the function definition file to see if an additional buffer is required. Optional function {API}_get_buffer_size() provides the buffer size if required. The caller is expected to clear the buffer, if applicable, for security reasons.
[in]	dw_conv_params	Depthwise convolution parameters (e.g. strides, dilations, pads,...) dw_conv_params->dilation is not used. Range of dw_conv_params->input_offset : [-127, 128] Range of dw_conv_params->output_offset : [-128, 127]
[in]	quant_params	Per-channel quantization info. It contains the multiplier and shift values to be applied to each output channel
[in]	input_dims	Input (activation) tensor dimensions. Format: [H, W, C_IN] Batch argument N is not used and assumed to be 1.
[in]	input_data	Input (activation) data pointer. Data type: int8
[in]	filter_dims	Filter tensor dimensions. Format: [1, H, W, C_OUT]
[in]	filter_data	Filter data pointer. Data type: int8
[in]	bias_dims	Bias tensor dimensions. Format: [C_OUT]
[in]	bias_data	Bias data pointer. Data type: int32
[in]	output_dims	Output tensor dimensions. Format: [1, H, W, C_OUT]
[in,out]	output_data	Output data pointer. Data type: int8

Returns

The function returns RISCV_NMSIS_NN_SUCCESS - Successful completion.

• Supported framework: TensorFlow Lite
• Picks one of the the following functions
  1. riscv_depthwise_conv_s8()
  2. riscv_depthwise_conv_3x3_s8() - RISC-V CPUs with DSP extension only
  3. riscv_depthwise_conv_s8_opt()
• Check details of riscv_depthwise_conv_s8_opt() for potential data that can be accessed outside of the boundary.

riscv_depthwise_separable_conv_HWC_q7()

riscv_nmsis_nn_status riscv_depthwise_separable_conv_HWC_q7	(	const q7_t *	Im_in,
		const uint16_t	dim_im_in,
		const uint16_t	ch_im_in,
		const q7_t *	wt,
		const uint16_t	ch_im_out,
		const uint16_t	dim_kernel,
		const uint16_t	padding,
		const uint16_t	stride,
		const q7_t *	bias,
		const uint16_t	bias_shift,
		const uint16_t	out_shift,
		q7_t *	Im_out,
		const uint16_t	dim_im_out,
		q15_t *	bufferA,
		q7_t *	bufferB
	)

Q7 depthwise separable convolution function.

Parameters

[in]	Im_in	pointer to input tensor
[in]	dim_im_in	input tensor dimension
[in]	ch_im_in	number of input tensor channels
[in]	wt	pointer to kernel weights
[in]	ch_im_out	number of filters, i.e., output tensor channels
[in]	dim_kernel	filter kernel size
[in]	padding	padding sizes
[in]	stride	convolution stride
[in]	bias	pointer to bias
[in]	bias_shift	amount of left-shift for bias
[in]	out_shift	amount of right-shift for output
[in,out]	Im_out	pointer to output tensor
[in]	dim_im_out	output tensor dimension
[in,out]	bufferA	pointer to buffer space for input
[in,out]	bufferB	pointer to buffer space for output

Returns

The function returns either RISCV_NMSIS_NN_SIZE_MISMATCH or RISCV_NMSIS_NN_SUCCESS based on the outcome of size checking.

Buffer size:

bufferA size: 2*ch_im_in*dim_kernel*dim_kernel

bufferB size: 0

Input dimension constraints:

ch_im_in equals ch_im_out

Implementation: There are 3 nested loop here: Inner loop: calculate each output value with MAC instruction over an accumulator Mid loop: loop over different output channel Outer loop: loop over different output (x, y)

riscv_depthwise_separable_conv_HWC_q7_nonsquare()

riscv_nmsis_nn_status riscv_depthwise_separable_conv_HWC_q7_nonsquare	(	const q7_t *	Im_in,
		const uint16_t	dim_im_in_x,
		const uint16_t	dim_im_in_y,
		const uint16_t	ch_im_in,
		const q7_t *	wt,
		const uint16_t	ch_im_out,
		const uint16_t	dim_kernel_x,
		const uint16_t	dim_kernel_y,
		const uint16_t	padding_x,
		const uint16_t	padding_y,
		const uint16_t	stride_x,
		const uint16_t	stride_y,
		const q7_t *	bias,
		const uint16_t	bias_shift,
		const uint16_t	out_shift,
		q7_t *	Im_out,
		const uint16_t	dim_im_out_x,
		const uint16_t	dim_im_out_y,
		q15_t *	bufferA,
		q7_t *	bufferB
	)

Q7 depthwise separable convolution function (non-square shape)

Parameters

[in]	Im_in	pointer to input tensor
[in]	dim_im_in_x	input tensor dimension x
[in]	dim_im_in_y	input tensor dimension y
[in]	ch_im_in	number of input tensor channels
[in]	wt	pointer to kernel weights
[in]	ch_im_out	number of filters, i.e., output tensor channels
[in]	dim_kernel_x	filter kernel size x
[in]	dim_kernel_y	filter kernel size y
[in]	padding_x	padding sizes x
[in]	padding_y	padding sizes y
[in]	stride_x	convolution stride x
[in]	stride_y	convolution stride y
[in]	bias	pointer to bias
[in]	bias_shift	amount of left-shift for bias
[in]	out_shift	amount of right-shift for output
[in,out]	Im_out	pointer to output tensor
[in]	dim_im_out_x	output tensor dimension x
[in]	dim_im_out_y	output tensor dimension y
[in,out]	bufferA	pointer to buffer space for input
[in,out]	bufferB	pointer to buffer space for output

Returns

The function returns either RISCV_NMSIS_NN_SIZE_MISMATCH or RISCV_NMSIS_NN_SUCCESS based on the outcome of size checking.

This function is the version with full list of optimization tricks, but with some constraints: ch_im_in is equal to ch_im_out

riscv_transpose_conv_s8()

riscv_nmsis_nn_status riscv_transpose_conv_s8	(	const nmsis_nn_context *	ctx,
		const nmsis_nn_context *	output_ctx,
		const nmsis_nn_transpose_conv_params *	transpose_conv_params,
		const nmsis_nn_per_channel_quant_params *	quant_params,
		const nmsis_nn_dims *	input_dims,
		const int8_t *	input_data,
		const nmsis_nn_dims *	filter_dims,
		const int8_t *	filter_data,
		const nmsis_nn_dims *	bias_dims,
		const int32_t *	bias_data,
		const nmsis_nn_dims *	output_dims,
		int8_t *	output_data
	)

Basic s8 transpose convolution function.

Parameters

[in,out]	ctx	Function context that contains the additional buffer if required by the function. riscv_transpose_conv_s8_get_buffer_size will return the buffer_size if required. The caller is expected to clear the buffer, if applicable, for security reasons.
[in,out]	output_ctx	Temporary scratch buffer. The size required size is: output width * output height * output channel * 4 The caller is expected to clear the buffer, if applicable, for security reasons.
[in]	transpose_conv_params	Convolution parameters (e.g. strides, dilations, pads,...). Range of transpose_conv_params->input_offset : [-127, 128] Range of transpose_conv_params->output_offset : [-128, 127]
[in]	quant_params	Per-channel quantization info. It contains the multiplier and shift values to be applied to each out channel.
[in]	input_dims	Input (activation) tensor dimensions. Format: [N, H, W, C_IN]
[in]	input_data	Input (activation) data pointer. Data type: int8
[in]	filter_dims	Filter tensor dimensions. Format: [C_OUT, HK, WK, C_IN] where HK and WK are the spatial filter dimensions
[in]	filter_data	Filter data pointer. Data type: int8
[in]	bias_dims	Bias tensor dimensions. Format: [C_OUT]
[in]	bias_data	Optional bias data pointer. Data type: int32
[in]	output_dims	Output tensor dimensions. Format: [N, H, W, C_OUT]
[out]	output_data	Output data pointer. Data type: int8

Returns

The function returns either RISCV_NMSIS_NN_ARG_ERROR if argument constraints fail. or, RISCV_NMSIS_NN_SUCCESS on successful completion.

1. Supported framework: TensorFlow Lite micro
2. Additional memory is required for optimization. Refer to arguments 'ctx' and 'output_ctx' for details.

riscv_transpose_conv_wrapper_s8()

riscv_nmsis_nn_status riscv_transpose_conv_wrapper_s8	(	const nmsis_nn_context *	ctx,
		const nmsis_nn_context *	output_ctx,
		const nmsis_nn_transpose_conv_params *	transpose_conv_params,
		const nmsis_nn_per_channel_quant_params *	quant_params,
		const nmsis_nn_dims *	input_dims,
		const int8_t *	input_data,
		const nmsis_nn_dims *	filter_dims,
		const int8_t *	filter_data,
		const nmsis_nn_dims *	bias_dims,
		const int32_t *	bias_data,
		const nmsis_nn_dims *	output_dims,
		int8_t *	output_data
	)

Wrapper to select optimal transposed convolution algorithm depending on parameters.

Parameters

[in,out]	ctx	Function context that contains the additional buffer if required by the function. riscv_transpose_conv_s8_get_buffer_size will return the buffer_size if required. The caller is expected to clear the buffer, if applicable, for security reasons.
[in,out]	output_ctx	Temporary scratch buffer. The size required size is: output width * output height * output channel * 4 The caller is expected to clear the buffer, if applicable, for security reasons.
[in]	transpose_conv_params	Convolution parameters (e.g. strides, dilations, pads,...). Range of transpose_conv_params->input_offset : [-127, 128] Range of transpose_conv_params->output_offset : [-128, 127]
[in]	quant_params	Per-channel quantization info. It contains the multiplier and shift values to be applied to each out channel.
[in]	input_dims	Input (activation) tensor dimensions. Format: [N, H, W, C_IN]
[in]	input_data	Input (activation) data pointer. Data type: int8
[in]	filter_dims	Filter tensor dimensions. Format: [C_OUT, HK, WK, C_IN] where HK and WK are the spatial filter dimensions
[in]	filter_data	Filter data pointer. Data type: int8
[in]	bias_dims	Bias tensor dimensions. Format: [C_OUT]
[in]	bias_data	Optional bias data pointer. Data type: int32
[in]	output_dims	Output tensor dimensions. Format: [N, H, W, C_OUT]
[out]	output_data	Output data pointer. Data type: int8

Returns

The function returns either RISCV_NMSIS_NN_ARG_ERROR if argument constraints fail. or, RISCV_NMSIS_NN_SUCCESS on successful completion.

1. Supported framework: TensorFlow Lite micro
2. Additional memory is required for optimization. Refer to arguments 'ctx' and 'output_ctx' for details.