Activation Functions

Perform activation layers, including ReLU (Rectified Linear Unit), sigmoid and tanh. More...

Functions
void	riscv_nn_activation_s16 (const int16_t *input, int16_t *output, const uint16_t size, const uint16_t left_shift, const riscv_nn_activation_type type)
	s16 neural network activation function using direct table look-up More...
void	riscv_nn_activations_direct_q15 (q15_t *data, uint16_t size, uint16_t int_width, riscv_nn_activation_type type)
	neural network activation function using direct table look-up More...
void	riscv_nn_activations_direct_q7 (q7_t *data, uint16_t size, uint16_t int_width, riscv_nn_activation_type type)
	Q7 neural network activation function using direct table look-up. More...
void	riscv_relu6_s8 (int8_t *data, uint16_t size)
	s8 ReLU6 function More...
void	riscv_relu_q15 (int16_t *data, uint16_t size)
	Q15 RELU function. More...
void	riscv_relu_q7 (int8_t *data, uint16_t size)
	Q7 RELU function. More...

Detailed Description

Perform activation layers, including ReLU (Rectified Linear Unit), sigmoid and tanh.

Function Documentation

riscv_nn_activation_s16()

void riscv_nn_activation_s16	(	const int16_t *	input,
		int16_t *	output,
		const uint16_t	size,
		const uint16_t	left_shift,
		const riscv_nn_activation_type	type
	)

s16 neural network activation function using direct table look-up

Parameters

[in]	input	pointer to input data
[out]	output	pointer to output
[in]	size	number of elements
[in]	left_shift	bit-width of the integer part, assume to be smaller than 3
[in]	type	type of activation functions

Supported framework: TensorFlow Lite for Microcontrollers. This activation function must be bit precise congruent with the corresponding TFLM tanh and sigmoid actication functions

riscv_nn_activations_direct_q15()

void riscv_nn_activations_direct_q15	(	q15_t *	data,
		uint16_t	size,
		uint16_t	int_width,
		riscv_nn_activation_type	type
	)

neural network activation function using direct table look-up

Q15 neural network activation function using direct table look-up.

Note

Refer header file for details.

riscv_nn_activations_direct_q7()

void riscv_nn_activations_direct_q7	(	q7_t *	data,
		uint16_t	size,
		uint16_t	int_width,
		riscv_nn_activation_type	type
	)

Q7 neural network activation function using direct table look-up.

Parameters

[in,out]	data	pointer to input
[in]	size	number of elements
[in]	int_width	bit-width of the integer part, assume to be smaller than 3
[in]	type	type of activation functions

This is the direct table look-up approach.

Assume here the integer part of the fixed-point is <= 3. More than 3 just not making much sense, makes no difference with saturation followed by any of these activation functions.

riscv_relu6_s8()

void riscv_relu6_s8	(	int8_t *	data,
		uint16_t	size
	)

s8 ReLU6 function

Parameters

[in,out]	data	pointer to input
[in]	size	number of elements

riscv_relu_q15()

void riscv_relu_q15	(	int16_t *	data,
		uint16_t	size
	)

Q15 RELU function.

Parameters

[in,out]	data	pointer to input
[in]	size	number of elements

riscv_relu_q7()

void riscv_relu_q7	(	int8_t *	data,
		uint16_t	size
	)

Q7 RELU function.

Parameters

[in,out]	data	pointer to input
[in]	size	number of elements