Concatenation Functions

Functions
void	arm_concatenation_s8_w (const int8_t *input, const uint16_t input_x, const uint16_t input_y, const uint16_t input_z, const uint16_t input_w, int8_t *output, const uint32_t offset_w)
	int8/uint8 concatenation function to be used for concatenating N-tensors along the W axis (Batch size) This function should be called for each input tensor to concatenate. The argument offset_w will be used to store the input tensor in the correct position in the output tensor
void	arm_concatenation_s8_x (const int8_t *input, const uint16_t input_x, const uint16_t input_y, const uint16_t input_z, const uint16_t input_w, int8_t *output, const uint16_t output_x, const uint32_t offset_x)
	int8/uint8 concatenation function to be used for concatenating N-tensors along the X axis This function should be called for each input tensor to concatenate. The argument offset_x will be used to store the input tensor in the correct position in the output tensor
void	arm_concatenation_s8_y (const int8_t *input, const uint16_t input_x, const uint16_t input_y, const uint16_t input_z, const uint16_t input_w, int8_t *output, const uint16_t output_y, const uint32_t offset_y)
	int8/uint8 concatenation function to be used for concatenating N-tensors along the Y axis This function should be called for each input tensor to concatenate. The argument offset_y will be used to store the input tensor in the correct position in the output tensor
void	arm_concatenation_s8_z (const int8_t *input, const uint16_t input_x, const uint16_t input_y, const uint16_t input_z, const uint16_t input_w, int8_t *output, const uint16_t output_z, const uint32_t offset_z)
	int8/uint8 concatenation function to be used for concatenating N-tensors along the Z axis This function should be called for each input tensor to concatenate. The argument offset_z will be used to store the input tensor in the correct position in the output tensor

Description

Function Documentation

arm_concatenation_s8_w()

void arm_concatenation_s8_w	(	const int8_t *	input,
		const uint16_t	input_x,
		const uint16_t	input_y,
		const uint16_t	input_z,
		const uint16_t	input_w,
		int8_t *	output,
		const uint32_t	offset_w
	)

int8/uint8 concatenation function to be used for concatenating N-tensors along the W axis (Batch size) This function should be called for each input tensor to concatenate. The argument offset_w will be used to store the input tensor in the correct position in the output tensor

i.e. offset_w = 0 for(i = 0 i < num_input_tensors; ++i) { arm_concatenation_s8_w(&input[i], ..., &output, ..., ..., offset_w) offset_w += input_w[i] }

This function assumes that the output tensor has:

1. The same width of the input tensor
2. The same height of the input tensor
3. The same number o channels of the input tensor

Unless specified otherwise, arguments are mandatory.

Note

This function, data layout independent, can be used to concatenate either int8 or uint8 tensors because it does not involve any arithmetic operation

Parameters

[in]	input	Pointer to input tensor
[in]	input_x	Width of input tensor
[in]	input_y	Height of input tensor
[in]	input_z	Channels in input tensor
[in]	input_w	Batch size in input tensor
[out]	output	Pointer to output tensor. Expected to be at least input_x * input_y * input_z * input_w bytes.
[in]	offset_w	The offset on the W axis to start concatenating the input tensor It is user responsibility to provide the correct value

arm_concatenation_s8_x()

void arm_concatenation_s8_x	(	const int8_t *	input,
		const uint16_t	input_x,
		const uint16_t	input_y,
		const uint16_t	input_z,
		const uint16_t	input_w,
		int8_t *	output,
		const uint16_t	output_x,
		const uint32_t	offset_x
	)

int8/uint8 concatenation function to be used for concatenating N-tensors along the X axis This function should be called for each input tensor to concatenate. The argument offset_x will be used to store the input tensor in the correct position in the output tensor

i.e. offset_x = 0 for(i = 0 i < num_input_tensors; ++i) { arm_concatenation_s8_x(&input[i], ..., &output, ..., ..., offset_x) offset_x += input_x[i] }

This function assumes that the output tensor has:

1. The same height of the input tensor
2. The same number of channels of the input tensor
3. The same batch size of the input tensor

Unless specified otherwise, arguments are mandatory.

Note

This function, data layout independent, can be used to concatenate either int8 or uint8 tensors because it does not involve any arithmetic operation

Parameters

[in]	input	Pointer to input tensor. Input tensor must not overlap with the output tensor.
[in]	input_x	Width of input tensor
[in]	input_y	Height of input tensor
[in]	input_z	Channels in input tensor
[in]	input_w	Batch size in input tensor
[out]	output	Pointer to output tensor. Expected to be at least (input_x * input_y * input_z * input_w) + offset_x bytes.
[in]	output_x	Width of output tensor
[in]	offset_x	The offset (in number of elements) on the X axis to start concatenating the input tensor It is user responsibility to provide the correct value

Input constraints offset_x is less than output_x

arm_concatenation_s8_y()

void arm_concatenation_s8_y	(	const int8_t *	input,
		const uint16_t	input_x,
		const uint16_t	input_y,
		const uint16_t	input_z,
		const uint16_t	input_w,
		int8_t *	output,
		const uint16_t	output_y,
		const uint32_t	offset_y
	)

int8/uint8 concatenation function to be used for concatenating N-tensors along the Y axis This function should be called for each input tensor to concatenate. The argument offset_y will be used to store the input tensor in the correct position in the output tensor

i.e. offset_y = 0 for(i = 0 i < num_input_tensors; ++i) { arm_concatenation_s8_y(&input[i], ..., &output, ..., ..., offset_y) offset_y += input_y[i] }

This function assumes that the output tensor has:

1. The same width of the input tensor
2. The same number of channels of the input tensor
3. The same batch size of the input tensor

Unless specified otherwise, arguments are mandatory.

Note

This function, data layout independent, can be used to concatenate either int8 or uint8 tensors because it does not involve any arithmetic operation

Parameters

[in]	input	Pointer to input tensor. Input tensor must not overlap with the output tensor.
[in]	input_x	Width of input tensor
[in]	input_y	Height of input tensor
[in]	input_z	Channels in input tensor
[in]	input_w	Batch size in input tensor
[out]	output	Pointer to output tensor. Expected to be at least (input_z * input_w * input_x * input_y) + offset_y bytes.
[in]	output_y	Height of output tensor
[in]	offset_y	The offset on the Y axis to start concatenating the input tensor It is user responsibility to provide the correct value

Input constraints offset_y is less than output_y

arm_concatenation_s8_z()

void arm_concatenation_s8_z	(	const int8_t *	input,
		const uint16_t	input_x,
		const uint16_t	input_y,
		const uint16_t	input_z,
		const uint16_t	input_w,
		int8_t *	output,
		const uint16_t	output_z,
		const uint32_t	offset_z
	)

int8/uint8 concatenation function to be used for concatenating N-tensors along the Z axis This function should be called for each input tensor to concatenate. The argument offset_z will be used to store the input tensor in the correct position in the output tensor

i.e. offset_z = 0 for(i = 0 i < num_input_tensors; ++i) { arm_concatenation_s8_z(&input[i], ..., &output, ..., ..., offset_z) offset_z += input_z[i] }

This function assumes that the output tensor has:

1. The same width of the input tensor
2. The same height of the input tensor
3. The same batch size of the input tensor

Unless specified otherwise, arguments are mandatory.

Note

This function, data layout independent, can be used to concatenate either int8 or uint8 tensors because it does not involve any arithmetic operation

Parameters

[in]	input	Pointer to input tensor. Input tensor must not overlap with output tensor.
[in]	input_x	Width of input tensor
[in]	input_y	Height of input tensor
[in]	input_z	Channels in input tensor
[in]	input_w	Batch size in input tensor
[out]	output	Pointer to output tensor. Expected to be at least (input_x * input_y * input_z * input_w) + offset_z bytes.
[in]	output_z	Channels in output tensor
[in]	offset_z	The offset on the Z axis to start concatenating the input tensor It is user responsibility to provide the correct value

Input constraints offset_z is less than output_z