libosmocore/src/core/conv_acc_neon_impl.h

/*! \file conv_acc_neon_impl.h
 * Accelerated Viterbi decoder implementation:
 * straight port of SSE to NEON based on Tom Tsous work */
/*
 * (C) 2020 by sysmocom - s.f.m.c. GmbH
 * Author: Eric Wild
 *
 * All Rights Reserved
 *
 * SPDX-License-Identifier: GPL-2.0+
 *
 * This program is free software; you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation; either version 2 of the License, or
 * (at your option) any later version.
 *
 * This program is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 */

/* Some distributions (notably Alpine Linux) for some strange reason
 * don't have this #define */
#ifndef __always_inline
#define __always_inline inline __attribute__((always_inline))
#endif

#define NEON_BUTTERFLY(M0,M1,M2,M3,M4) \
{ \
	M3 = vqaddq_s16(M0, M2); \
	M4 = vqsubq_s16(M1, M2); \
	M0 = vqsubq_s16(M0, M2); \
	M1 = vqaddq_s16(M1, M2); \
	M2 = vmaxq_s16(M3, M4); \
	M3 = vreinterpretq_s16_u16(vcgtq_s16(M3, M4)); \
	M4 = vmaxq_s16(M0, M1); \
	M1 = vreinterpretq_s16_u16(vcgtq_s16(M0, M1)); \
}

#define NEON_DEINTERLEAVE_K5(M0,M1,M2,M3) \
{ \
	int16x8x2_t tmp; \
	tmp = vuzpq_s16(M0, M1); \
	M2 = tmp.val[0]; \
	M3 = tmp.val[1]; \
}

#define NEON_DEINTERLEAVE_K7(M0,M1,M2,M3,M4,M5,M6,M7,M8,M9,M10,M11,M12,M13,M14,M15) \
{ \
	int16x8x2_t tmp; \
	tmp = vuzpq_s16(M0, M1); \
	M8 = tmp.val[0]; M9 = tmp.val[1]; \
	tmp = vuzpq_s16(M2, M3); \
	M10 = tmp.val[0]; M11 = tmp.val[1]; \
	tmp = vuzpq_s16(M4, M5); \
	M12 = tmp.val[0]; M13 = tmp.val[1]; \
	tmp = vuzpq_s16(M6, M7); \
	M14 = tmp.val[0]; M15 = tmp.val[1]; \
}

#define NEON_BRANCH_METRIC_N2(M0,M1,M2,M3,M4,M6,M7) \
{ \
	M0 = vmulq_s16(M4, M0); \
	M1 = vmulq_s16(M4, M1); \
	M2 = vmulq_s16(M4, M2); \
	M3 = vmulq_s16(M4, M3); \
	M6 = vcombine_s16(vpadd_s16(vget_low_s16(M0), vget_high_s16(M0)), vpadd_s16(vget_low_s16(M1), vget_high_s16(M1))); \
	M7 = vcombine_s16(vpadd_s16(vget_low_s16(M2), vget_high_s16(M2)), vpadd_s16(vget_low_s16(M3), vget_high_s16(M3))); \
}

#define NEON_BRANCH_METRIC_N4(M0,M1,M2,M3,M4,M5) \
{ \
	M0 = vmulq_s16(M4, M0); \
	M1 = vmulq_s16(M4, M1); \
	M2 = vmulq_s16(M4, M2); \
	M3 = vmulq_s16(M4, M3); \
	int16x4_t t1 = vpadd_s16(vpadd_s16(vget_low_s16(M0), vget_high_s16(M0)), vpadd_s16(vget_low_s16(M1), vget_high_s16(M1))); \
	int16x4_t t2 = vpadd_s16(vpadd_s16(vget_low_s16(M2), vget_high_s16(M2)), vpadd_s16(vget_low_s16(M3), vget_high_s16(M3))); \
	M5 = vcombine_s16(t1, t2); \
}

#define NEON_NORMALIZE_K5(M0,M1,M2,M3) \
{ \
	M2 = vminq_s16(M0, M1); \
	int16x4_t t = vpmin_s16(vget_low_s16(M2), vget_high_s16(M2)); \
	t = vpmin_s16(t, t); \
	t = vpmin_s16(t, t); \
	M2 = vdupq_lane_s16(t, 0); \
	M0 = vqsubq_s16(M0, M2); \
	M1 = vqsubq_s16(M1, M2); \
}

#define NEON_NORMALIZE_K7(M0,M1,M2,M3,M4,M5,M6,M7,M8,M9,M10,M11) \
{ \
	M8 = vminq_s16(M0, M1); \
	M9 = vminq_s16(M2, M3); \
	M10 = vminq_s16(M4, M5); \
	M11 = vminq_s16(M6, M7); \
	M8 = vminq_s16(M8, M9); \
	M10 = vminq_s16(M10, M11); \
	M8 = vminq_s16(M8, M10); \
	int16x4_t t = vpmin_s16(vget_low_s16(M8), vget_high_s16(M8)); \
	t = vpmin_s16(t, t); \
	t = vpmin_s16(t, t); \
	M8 = vdupq_lane_s16(t, 0); \
	M0 = vqsubq_s16(M0, M8); \
	M1 = vqsubq_s16(M1, M8); \
	M2 = vqsubq_s16(M2, M8); \
	M3 = vqsubq_s16(M3, M8); \
	M4 = vqsubq_s16(M4, M8); \
	M5 = vqsubq_s16(M5, M8); \
	M6 = vqsubq_s16(M6, M8); \
	M7 = vqsubq_s16(M7, M8); \
}

__always_inline void _neon_metrics_k5_n2(const int16_t *val, const int16_t *outa, int16_t *sumsa, int16_t *paths,
					 int norm)
{
	int16_t *__restrict out = __builtin_assume_aligned(outa, 8);
	int16_t *__restrict sums = __builtin_assume_aligned(sumsa, 8);
	int16x8_t m0, m1, m2, m3, m4, m5, m6;
	int16x4_t input;

	/* (BMU) Load and expand 8-bit input out to 16-bits */
	input = vld1_s16(val);
	m2 = vcombine_s16(input, input);

	/* (BMU) Load and compute branch metrics */
	m0 = vld1q_s16(&out[0]);
	m1 = vld1q_s16(&out[8]);

	m0 = vmulq_s16(m2, m0);
	m1 = vmulq_s16(m2, m1);
	m2 = vcombine_s16(vpadd_s16(vget_low_s16(m0), vget_high_s16(m0)),
			  vpadd_s16(vget_low_s16(m1), vget_high_s16(m1)));

	/* (PMU) Load accumulated path matrics */
	m0 = vld1q_s16(&sums[0]);
	m1 = vld1q_s16(&sums[8]);

	NEON_DEINTERLEAVE_K5(m0, m1, m3, m4)

	/* (PMU) Butterflies: 0-7 */
	NEON_BUTTERFLY(m3, m4, m2, m5, m6)

	if (norm)
		NEON_NORMALIZE_K5(m2, m6, m0, m1)

	vst1q_s16(&sums[0], m2);
	vst1q_s16(&sums[8], m6);
	vst1q_s16(&paths[0], m5);
	vst1q_s16(&paths[8], m4);
}

__always_inline void _neon_metrics_k5_n4(const int16_t *val, const int16_t *outa, int16_t *sumsa, int16_t *paths,
					 int norm)
{
	int16_t *__restrict out = __builtin_assume_aligned(outa, 8);
	int16_t *__restrict sums = __builtin_assume_aligned(sumsa, 8);
	int16x8_t m0, m1, m2, m3, m4, m5, m6;
	int16x4_t input;

	/* (BMU) Load and expand 8-bit input out to 16-bits */
	input = vld1_s16(val);
	m4 = vcombine_s16(input, input);

	/* (BMU) Load and compute branch metrics */
	m0 = vld1q_s16(&out[0]);
	m1 = vld1q_s16(&out[8]);
	m2 = vld1q_s16(&out[16]);
	m3 = vld1q_s16(&out[24]);

	NEON_BRANCH_METRIC_N4(m0, m1, m2, m3, m4, m2)

	/* (PMU) Load accumulated path matrics */
	m0 = vld1q_s16(&sums[0]);
	m1 = vld1q_s16(&sums[8]);

	NEON_DEINTERLEAVE_K5(m0, m1, m3, m4)

	/* (PMU) Butterflies: 0-7 */
	NEON_BUTTERFLY(m3, m4, m2, m5, m6)

	if (norm)
		NEON_NORMALIZE_K5(m2, m6, m0, m1)

	vst1q_s16(&sums[0], m2);
	vst1q_s16(&sums[8], m6);
	vst1q_s16(&paths[0], m5);
	vst1q_s16(&paths[8], m4);
}

__always_inline static void _neon_metrics_k7_n2(const int16_t *val, const int16_t *outa, int16_t *sumsa, int16_t *paths,
						int norm)
{
	int16_t *__restrict out = __builtin_assume_aligned(outa, 8);
	int16_t *__restrict sums = __builtin_assume_aligned(sumsa, 8);
	int16x8_t m0, m1, m2, m3, m4, m5, m6, m7;
	int16x8_t m8, m9, m10, m11, m12, m13, m14, m15;
	int16x4_t input;

	/* (PMU) Load accumulated path matrics */
	m0 = vld1q_s16(&sums[0]);
	m1 = vld1q_s16(&sums[8]);
	m2 = vld1q_s16(&sums[16]);
	m3 = vld1q_s16(&sums[24]);
	m4 = vld1q_s16(&sums[32]);
	m5 = vld1q_s16(&sums[40]);
	m6 = vld1q_s16(&sums[48]);
	m7 = vld1q_s16(&sums[56]);

	/* (PMU) Deinterleave into even and odd packed registers */
	NEON_DEINTERLEAVE_K7(m0, m1, m2, m3, m4, m5, m6, m7, m8, m9, m10, m11, m12, m13, m14, m15)

	/* (BMU) Load and expand 8-bit input out to 16-bits */
	input = vld1_s16(val);
	m7 = vcombine_s16(input, input);

	/* (BMU) Load and compute branch metrics */
	m0 = vld1q_s16(&out[0]);
	m1 = vld1q_s16(&out[8]);
	m2 = vld1q_s16(&out[16]);
	m3 = vld1q_s16(&out[24]);

	NEON_BRANCH_METRIC_N2(m0, m1, m2, m3, m7, m4, m5)

	m0 = vld1q_s16(&out[32]);
	m1 = vld1q_s16(&out[40]);
	m2 = vld1q_s16(&out[48]);
	m3 = vld1q_s16(&out[56]);

	NEON_BRANCH_METRIC_N2(m0, m1, m2, m3, m7, m6, m7)

	/* (PMU) Butterflies: 0-15 */
	NEON_BUTTERFLY(m8, m9, m4, m0, m1)
	NEON_BUTTERFLY(m10, m11, m5, m2, m3)

	vst1q_s16(&paths[0], m0);
	vst1q_s16(&paths[8], m2);
	vst1q_s16(&paths[32], m9);
	vst1q_s16(&paths[40], m11);

	/* (PMU) Butterflies: 17-31 */
	NEON_BUTTERFLY(m12, m13, m6, m0, m2)
	NEON_BUTTERFLY(m14, m15, m7, m9, m11)

	vst1q_s16(&paths[16], m0);
	vst1q_s16(&paths[24], m9);
	vst1q_s16(&paths[48], m13);
	vst1q_s16(&paths[56], m15);

	if (norm)
		NEON_NORMALIZE_K7(m4, m1, m5, m3, m6, m2, m7, m11, m0, m8, m9, m10)

	vst1q_s16(&sums[0], m4);
	vst1q_s16(&sums[8], m5);
	vst1q_s16(&sums[16], m6);
	vst1q_s16(&sums[24], m7);
	vst1q_s16(&sums[32], m1);
	vst1q_s16(&sums[40], m3);
	vst1q_s16(&sums[48], m2);
	vst1q_s16(&sums[56], m11);
}

__always_inline static void _neon_metrics_k7_n4(const int16_t *val, const int16_t *outa, int16_t *sumsa, int16_t *paths,
						int norm)
{
	int16_t *__restrict out = __builtin_assume_aligned(outa, 8);
	int16_t *__restrict sums = __builtin_assume_aligned(sumsa, 8);
	int16x8_t m0, m1, m2, m3, m4, m5, m6, m7;
	int16x8_t m8, m9, m10, m11, m12, m13, m14, m15;
	int16x4_t input;

	/* (PMU) Load accumulated path matrics */
	m0 = vld1q_s16(&sums[0]);
	m1 = vld1q_s16(&sums[8]);
	m2 = vld1q_s16(&sums[16]);
	m3 = vld1q_s16(&sums[24]);
	m4 = vld1q_s16(&sums[32]);
	m5 = vld1q_s16(&sums[40]);
	m6 = vld1q_s16(&sums[48]);
	m7 = vld1q_s16(&sums[56]);

	/* (PMU) Deinterleave into even and odd packed registers */
	NEON_DEINTERLEAVE_K7(m0, m1, m2, m3, m4, m5, m6, m7, m8, m9, m10, m11, m12, m13, m14, m15)

	/* (BMU) Load and expand 8-bit input out to 16-bits */
	input = vld1_s16(val);
	m7 = vcombine_s16(input, input);

	/* (BMU) Load and compute branch metrics */
	m0 = vld1q_s16(&out[0]);
	m1 = vld1q_s16(&out[8]);
	m2 = vld1q_s16(&out[16]);
	m3 = vld1q_s16(&out[24]);

	NEON_BRANCH_METRIC_N4(m0, m1, m2, m3, m7, m4)

	m0 = vld1q_s16(&out[32]);
	m1 = vld1q_s16(&out[40]);
	m2 = vld1q_s16(&out[48]);
	m3 = vld1q_s16(&out[56]);

	NEON_BRANCH_METRIC_N4(m0, m1, m2, m3, m7, m5)

	m0 = vld1q_s16(&out[64]);
	m1 = vld1q_s16(&out[72]);
	m2 = vld1q_s16(&out[80]);
	m3 = vld1q_s16(&out[88]);

	NEON_BRANCH_METRIC_N4(m0, m1, m2, m3, m7, m6)

	m0 = vld1q_s16(&out[96]);
	m1 = vld1q_s16(&out[104]);
	m2 = vld1q_s16(&out[112]);
	m3 = vld1q_s16(&out[120]);

	NEON_BRANCH_METRIC_N4(m0, m1, m2, m3, m7, m7)

	/* (PMU) Butterflies: 0-15 */
	NEON_BUTTERFLY(m8, m9, m4, m0, m1)
	NEON_BUTTERFLY(m10, m11, m5, m2, m3)

	vst1q_s16(&paths[0], m0);
	vst1q_s16(&paths[8], m2);
	vst1q_s16(&paths[32], m9);
	vst1q_s16(&paths[40], m11);

	/* (PMU) Butterflies: 17-31 */
	NEON_BUTTERFLY(m12, m13, m6, m0, m2)
	NEON_BUTTERFLY(m14, m15, m7, m9, m11)

	vst1q_s16(&paths[16], m0);
	vst1q_s16(&paths[24], m9);
	vst1q_s16(&paths[48], m13);
	vst1q_s16(&paths[56], m15);

	if (norm)
		NEON_NORMALIZE_K7(m4, m1, m5, m3, m6, m2, m7, m11, m0, m8, m9, m10)

	vst1q_s16(&sums[0], m4);
	vst1q_s16(&sums[8], m5);
	vst1q_s16(&sums[16], m6);
	vst1q_s16(&sums[24], m7);
	vst1q_s16(&sums[32], m1);
	vst1q_s16(&sums[40], m3);
	vst1q_s16(&sums[48], m2);
	vst1q_s16(&sums[56], m11);
}