Softmax Functions

Functions
arm_cmsis_nn_status	arm_softmax_s16 (const int16_t *input, const int32_t num_rows, const int32_t row_size, const int32_t mult, const int32_t shift, const cmsis_nn_softmax_lut_s16 *softmax_params, int16_t *output)
	S16 softmax function. More...
void	arm_softmax_s8 (const int8_t *input, const int32_t num_rows, const int32_t row_size, const int32_t mult, const int32_t shift, const int32_t diff_min, int8_t *output)
	S8 softmax function. More...
void	arm_softmax_s8_s16 (const int8_t *input, const int32_t num_rows, const int32_t row_size, const int32_t mult, const int32_t shift, const int32_t diff_min, int16_t *output)
	S8 to s16 softmax function. More...
void	arm_softmax_u8 (const uint8_t *input, const int32_t num_rows, const int32_t row_size, const int32_t mult, const int32_t shift, const int32_t diff_min, uint8_t *output)
	U8 softmax function. More...

Description

Function Documentation

arm_softmax_s16()

arm_cmsis_nn_status arm_softmax_s16	(	const int16_t *	input,
		const int32_t	num_rows,
		const int32_t	row_size,
		const int32_t	mult,
		const int32_t	shift,
		const cmsis_nn_softmax_lut_s16 *	softmax_params,
		int16_t *	output
	)

Parameters

[in]	input	Pointer to the input tensor
[in]	num_rows	Number of rows in the input tensor
[in]	row_size	Number of elements in each input row
[in]	mult	Input quantization multiplier
[in]	shift	Input quantization shift within the range [0, 31]
[in]	softmax_params	Softmax s16 layer parameters with two pointers to LUTs speficied below. For indexing the high 9 bits are used and 7 remaining for interpolation. That means 512 entries for the 9-bit indexing and 1 extra for interpolation, i.e. 513 values for each LUT. Lookup table for exp(x), where x uniform distributed between [-10.0 , 0.0] Lookup table for 1 / (1 + x), where x uniform distributed between [0.0 , 1.0]
[out]	output	Pointer to the output tensor

Returns

The function returns ARM_CMSIS_NN_ARG_ERROR Argument error check failed ARM_CMSIS_NN_SUCCESS - Successful operation

Note

Supported framework: TensorFlow Lite micro (bit-accurate)

arm_softmax_s8()

void arm_softmax_s8	(	const int8_t *	input,
		const int32_t	num_rows,
		const int32_t	row_size,
		const int32_t	mult,
		const int32_t	shift,
		const int32_t	diff_min,
		int8_t *	output
	)

Parameters

[in]	input	Pointer to the input tensor
[in]	num_rows	Number of rows in the input tensor
[in]	row_size	Number of elements in each input row
[in]	mult	Input quantization multiplier
[in]	shift	Input quantization shift within the range [0, 31]
[in]	diff_min	Minimum difference with max in row. Used to check if the quantized exponential operation can be performed
[out]	output	Pointer to the output tensor

Note

Supported framework: TensorFlow Lite micro (bit-accurate)

arm_softmax_s8_s16()

void arm_softmax_s8_s16	(	const int8_t *	input,
		const int32_t	num_rows,
		const int32_t	row_size,
		const int32_t	mult,
		const int32_t	shift,
		const int32_t	diff_min,
		int16_t *	output
	)

Parameters

[in]	input	Pointer to the input tensor
[in]	num_rows	Number of rows in the input tensor
[in]	row_size	Number of elements in each input row
[in]	mult	Input quantization multiplier
[in]	shift	Input quantization shift within the range [0, 31]
[in]	diff_min	Minimum difference with max in row. Used to check if the quantized exponential operation can be performed
[out]	output	Pointer to the output tensor

Note

Supported framework: TensorFlow Lite micro (bit-accurate)

arm_softmax_u8()

void arm_softmax_u8	(	const uint8_t *	input,
		const int32_t	num_rows,
		const int32_t	row_size,
		const int32_t	mult,
		const int32_t	shift,
		const int32_t	diff_min,
		uint8_t *	output
	)

Parameters

[in]	input	Pointer to the input tensor
[in]	num_rows	Number of rows in the input tensor
[in]	row_size	Number of elements in each input row
[in]	mult	Input quantization multiplier
[in]	shift	Input quantization shift within the range [0, 31]
[in]	diff_min	Minimum difference with max in row. Used to check if the quantized exponential operation can be performed
[out]	output	Pointer to the output tensor

Note

Supported framework: TensorFlow Lite micro (bit-accurate)