Pooling Functions

Perform pooling functions, including max pooling and average pooling. More...

Modules
	GetBufferSizePooling

Functions
riscv_nmsis_nn_status	riscv_avgpool_s16 (const nmsis_nn_context *ctx, const nmsis_nn_pool_params *pool_params, const nmsis_nn_dims *input_dims, const int16_t *src, const nmsis_nn_dims *filter_dims, const nmsis_nn_dims *output_dims, int16_t *dst)
	s16 average pooling function. More...
riscv_nmsis_nn_status	riscv_avgpool_s8 (const nmsis_nn_context *ctx, const nmsis_nn_pool_params *pool_params, const nmsis_nn_dims *input_dims, const int8_t *src, const nmsis_nn_dims *filter_dims, const nmsis_nn_dims *output_dims, int8_t *dst)
	s8 average pooling function. More...
riscv_nmsis_nn_status	riscv_max_pool_s16 (const nmsis_nn_context *ctx, const nmsis_nn_pool_params *pool_params, const nmsis_nn_dims *input_dims, const int16_t *src, const nmsis_nn_dims *filter_dims, const nmsis_nn_dims *output_dims, int16_t *dst)
	s16 max pooling function. More...
riscv_nmsis_nn_status	riscv_max_pool_s8 (const nmsis_nn_context *ctx, const nmsis_nn_pool_params *pool_params, const nmsis_nn_dims *input_dims, const int8_t *src, const nmsis_nn_dims *filter_dims, const nmsis_nn_dims *output_dims, int8_t *dst)
	s8 max pooling function. More...
void	riscv_maxpool_q7_HWC (q7_t *Im_in, const uint16_t dim_im_in, const uint16_t ch_im_in, const uint16_t dim_kernel, const uint16_t padding, const uint16_t stride, const uint16_t dim_im_out, q7_t *bufferA, q7_t *Im_out)
	Q7 max pooling function. More...
void	riscv_avepool_q7_HWC (q7_t *Im_in, const uint16_t dim_im_in, const uint16_t ch_im_in, const uint16_t dim_kernel, const uint16_t padding, const uint16_t stride, const uint16_t dim_im_out, q7_t *bufferA, q7_t *Im_out)
	Q7 average pooling function. More...

Detailed Description

Perform pooling functions, including max pooling and average pooling.

Perform max and average pooling operations.

Function Documentation

riscv_avepool_q7_HWC()

void riscv_avepool_q7_HWC	(	q7_t *	Im_in,
		const uint16_t	dim_im_in,
		const uint16_t	ch_im_in,
		const uint16_t	dim_kernel,
		const uint16_t	padding,
		const uint16_t	stride,
		const uint16_t	dim_im_out,
		q7_t *	bufferA,
		q7_t *	Im_out
	)

Q7 average pooling function.

Parameters

[in,out]	Im_in	pointer to input tensor
[in]	dim_im_in	input tensor dimention
[in]	ch_im_in	number of input tensor channels
[in]	dim_kernel	filter kernel size
[in]	padding	padding sizes
[in]	stride	convolution stride
[in]	dim_im_out	output tensor dimension
[in,out]	bufferA	pointer to buffer space for input
[in,out]	Im_out	pointer to output tensor

Buffer size:

bufferA size: 2*dim_im_out*ch_im_in

The pooling function is implemented as split x-pooling then y-pooling.

This pooling function is input-destructive. Input data is undefined after calling this function.

riscv_avgpool_s16()

riscv_nmsis_nn_status riscv_avgpool_s16	(	const nmsis_nn_context *	ctx,
		const nmsis_nn_pool_params *	pool_params,
		const nmsis_nn_dims *	input_dims,
		const int16_t *	input_data,
		const nmsis_nn_dims *	filter_dims,
		const nmsis_nn_dims *	output_dims,
		int16_t *	output_data
	)

s16 average pooling 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 an additional buffer is required. The caller is expected to clear the buffer ,if applicable, for security reasons.
[in]	pool_params	Pooling parameters
[in]	input_dims	Input (activation) tensor dimensions. Format: [H, W, C_IN] Argument 'N' is not used.
[in]	input_data	Input (activation) data pointer. Data type: int16
[in]	filter_dims	Filter tensor dimensions. Format: [H, W] Argument N and C are not used.
[in]	output_dims	Output tensor dimensions. Format: [H, W, C_OUT] Argument N is not used. C_OUT equals C_IN.
[in,out]	output_data	Output data pointer. Data type: int16

Returns

The function returns RISCV_NMSIS_NN_SUCCESS - Successful operation RISCV_NMSIS_NN_ARG_ERROR - In case of invalid arguments

• Supported Framework: TensorFlow Lite

riscv_avgpool_s8()

riscv_nmsis_nn_status riscv_avgpool_s8	(	const nmsis_nn_context *	ctx,
		const nmsis_nn_pool_params *	pool_params,
		const nmsis_nn_dims *	input_dims,
		const int8_t *	input_data,
		const nmsis_nn_dims *	filter_dims,
		const nmsis_nn_dims *	output_dims,
		int8_t *	output_data
	)

s8 average pooling 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 an additional buffer is required. The caller is expected to clear the buffer ,if applicable, for security reasons.
[in]	pool_params	Pooling parameters
[in]	input_dims	Input (activation) tensor dimensions. Format: [H, W, C_IN] Argument 'N' is not used.
[in]	input_data	Input (activation) data pointer. Data type: int8
[in]	filter_dims	Filter tensor dimensions. Format: [H, W] Argument N and C are not used.
[in]	output_dims	Output tensor dimensions. Format: [H, W, C_OUT] Argument N is not used. C_OUT equals C_IN.
[in,out]	output_data	Output data pointer. Data type: int8

Returns

The function returns RISCV_NMSIS_NN_SUCCESS - Successful operation

• Supported Framework: TensorFlow Lite

riscv_max_pool_s16()

riscv_nmsis_nn_status riscv_max_pool_s16	(	const nmsis_nn_context *	ctx,
		const nmsis_nn_pool_params *	pool_params,
		const nmsis_nn_dims *	input_dims,
		const int16_t *	src,
		const nmsis_nn_dims *	filter_dims,
		const nmsis_nn_dims *	output_dims,
		int16_t *	dst
	)

s16 max pooling 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 an additional buffer is required. The caller is expected to clear the buffer ,if applicable, for security reasons.
[in]	pool_params	Pooling parameters
[in]	input_dims	Input (activation) tensor dimensions. Format: [H, W, C_IN] Argument 'N' is not used.
[in]	src	Input (activation) data pointer. The input tensor must not overlap with the output tensor. Data type: int16
[in]	filter_dims	Filter tensor dimensions. Format: [H, W] Argument N and C are not used.
[in]	output_dims	Output tensor dimensions. Format: [H, W, C_OUT] Argument N is not used. C_OUT equals C_IN.
[in,out]	dst	Output data pointer. Data type: int16

Returns

The function returns RISCV_NMSIS_NN_SUCCESS - Successful operation

• Supported Framework: TensorFlow Lite

riscv_max_pool_s8()

riscv_nmsis_nn_status riscv_max_pool_s8	(	const nmsis_nn_context *	ctx,
		const nmsis_nn_pool_params *	pool_params,
		const nmsis_nn_dims *	input_dims,
		const int8_t *	input_data,
		const nmsis_nn_dims *	filter_dims,
		const nmsis_nn_dims *	output_dims,
		int8_t *	output_data
	)

s8 max pooling 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 an additional buffer is required. The caller is expected to clear the buffer ,if applicable, for security reasons.
[in]	pool_params	Pooling parameters
[in]	input_dims	Input (activation) tensor dimensions. Format: [H, W, C_IN] Argument 'N' is not used.
[in]	input_data	Input (activation) data pointer. The input tensor must not overlap with the output tensor. Data type: int8
[in]	filter_dims	Filter tensor dimensions. Format: [H, W] Argument N and C are not used.
[in]	output_dims	Output tensor dimensions. Format: [H, W, C_OUT] Argument N is not used. C_OUT equals C_IN.
[in,out]	output_data	Output data pointer. Data type: int8

Returns

The function returns RISCV_NMSIS_NN_SUCCESS - Successful operation

• Supported Framework: TensorFlow Lite

riscv_maxpool_q7_HWC()

void riscv_maxpool_q7_HWC	(	q7_t *	Im_in,
		const uint16_t	dim_im_in,
		const uint16_t	ch_im_in,
		const uint16_t	dim_kernel,
		const uint16_t	padding,
		const uint16_t	stride,
		const uint16_t	dim_im_out,
		q7_t *	bufferA,
		q7_t *	Im_out
	)

Q7 max pooling function.

Parameters

[in,out]	Im_in	pointer to input tensor
[in]	dim_im_in	input tensor dimention
[in]	ch_im_in	number of input tensor channels
[in]	dim_kernel	filter kernel size
[in]	padding	padding sizes
[in]	stride	convolution stride
[in]	dim_im_out	output tensor dimension
[in,out]	bufferA	Not used
[in,out]	Im_out	pointer to output tensor

The pooling function is implemented as split x-pooling then y-pooling.

This pooling function is input-destructive. Input data is undefined after calling this function.