arm_nn_mat_mul_kernel_s16.c File Reference

Functions
q15_t *	arm_nn_mat_mult_kernel_s16 (const q7_t *input_a, const q15_t *input_b, const int32_t output_ch, const int32_t *out_shift, const int32_t *out_mult, const int16_t activation_min, const int16_t activation_max, const int32_t num_col_a, const int64_t *const output_bias, q15_t *out_0)
	Matrix-multiplication function for convolution with per-channel requantization for 16 bits convolution. More...

Function Documentation

q15_t* arm_nn_mat_mult_kernel_s16	(	const q7_t *	input_a,
		const q15_t *	input_b,
		const int32_t	output_ch,
		const int32_t *	out_shift,
		const int32_t *	out_mult,
		const int16_t	activation_min,
		const int16_t	activation_max,
		const int32_t	num_col_a,
		const int64_t *const	output_bias,
		q15_t *	out_0
	)

Parameters

[in]	input_a	pointer to operand A
[in]	input_b	pointer to operand B, always consists of 2 vectors.
[in]	output_ch	number of rows of A
[in]	out_shift	pointer to per output channel requantization shift parameter.
[in]	out_mult	pointer to per output channel requantization multiplier parameter.
[in]	activation_min	minimum value to clamp the output to. Range : int16
[in]	activation_max	maximum value to clamp the output to. Range : int16
[in]	num_col_a	number of columns of A
[in]	output_bias	per output channel bias. Range : int64
[in,out]	out_0	pointer to output

Returns

The function returns one of the two

1. The incremented output pointer for a successful operation or
2. NULL if implementation is not available.

This function does the matrix multiplication of weight matrix for all output channels with 2 columns from im2col and produces two elements/output_channel. The outputs are clamped in the range provided by activation min and max. Supported framework: TensorFlow Lite micro.

References arm_nn_read_q15x2_ia(), arm_nn_requantize(), arm_nn_requantize_s64(), MAX, MIN, and REDUCE_MULTIPLIER.

Referenced by arm_convolve_fast_s16().