aboutsummaryrefslogtreecommitdiff
path: root/src/core/NEON/kernels/NEGEMMLowpMatrixMultiplyKernel.cpp
blob: 8f5a208cbbeb74fb942d89d203b776c10b8df8d3 (plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
/*
 * Copyright (c) 2017-2019 ARM Limited.
 *
 * SPDX-License-Identifier: MIT
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy
 * of this software and associated documentation files (the "Software"), to
 * deal in the Software without restriction, including without limitation the
 * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
 * sell copies of the Software, and to permit persons to whom the Software is
 * furnished to do so, subject to the following conditions:
 *
 * The above copyright notice and this permission notice shall be included in all
 * copies or substantial portions of the Software.
 *
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 * SOFTWARE.
 */
#include "arm_compute/core/NEON/kernels/NEGEMMLowpMatrixMultiplyKernel.h"

#include "arm_compute/core/AccessWindowStatic.h"
#include "arm_compute/core/Error.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/ITensor.h"
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/Utils.h"
#include "arm_compute/core/Validate.h"
#include "arm_compute/core/Window.h"

#include <arm_neon.h>
#include <cstddef>
#include <cstdint>
#include <tuple>

using namespace arm_compute;

namespace arm_compute
{
namespace
{
void inline vector_matrix_multiply_u8(Iterator &ina, Iterator &inb, Iterator &out, int width_a, int width_b, size_t stride_b, const Window &window)
{
    execute_window_loop(window, [&](const Coordinates & id)
    {
        if(id.x() > width_b)
        {
            return;
        }

        // Note: Since the input are all positives, we can use uint32_t
        // Accumulators for the block 0
        uint32x4x4_t c0 =
        {
            {
                vdupq_n_u32(0),
                vdupq_n_u32(0),
                vdupq_n_u32(0),
                vdupq_n_u32(0)
            }
        };

        auto vec_a          = reinterpret_cast<const uint8_t *>(ina.ptr());
        auto matrix_b       = reinterpret_cast<const uint8_t *>(inb.ptr());
        auto vec_a_end_addr = vec_a + width_a;

        // This for loop performs 8 accumulations
        for(; vec_a <= (vec_a_end_addr - 8);)
        {
            const uint8x8_t  a00_u8 = vld1_u8(vec_a);
            const uint8x16_t b00_u8 = vld1q_u8(matrix_b + 0 * stride_b);
            const uint8x16_t b10_u8 = vld1q_u8(matrix_b + 1 * stride_b);
            const uint8x16_t b20_u8 = vld1q_u8(matrix_b + 2 * stride_b);
            const uint8x16_t b30_u8 = vld1q_u8(matrix_b + 3 * stride_b);
            const uint8x16_t b40_u8 = vld1q_u8(matrix_b + 4 * stride_b);
            const uint8x16_t b50_u8 = vld1q_u8(matrix_b + 5 * stride_b);
            const uint8x16_t b60_u8 = vld1q_u8(matrix_b + 6 * stride_b);
            const uint8x16_t b70_u8 = vld1q_u8(matrix_b + 7 * stride_b);

            // Convert a00_u8 to uint16_t and get the lower part
            const uint16x4x2_t a00_u16 =
            {
                {
                    vget_low_u16(vmovl_u8(a00_u8)),
                    vget_high_u16(vmovl_u8(a00_u8))
                }
            };

            const uint16x4x4_t b00_u16 =
            {
                {
                    vget_low_u16(vmovl_u8(vget_low_u8(b00_u8))),
                    vget_high_u16(vmovl_u8(vget_low_u8(b00_u8))),
                    vget_low_u16(vmovl_u8(vget_high_u8(b00_u8))),
                    vget_high_u16(vmovl_u8(vget_high_u8(b00_u8)))
                }
            };

            const uint16x4x4_t b10_u16 =
            {
                {
                    vget_low_u16(vmovl_u8(vget_low_u8(b10_u8))),
                    vget_high_u16(vmovl_u8(vget_low_u8(b10_u8))),
                    vget_low_u16(vmovl_u8(vget_high_u8(b10_u8))),
                    vget_high_u16(vmovl_u8(vget_high_u8(b10_u8)))
                }
            };

            const uint16x4x4_t b20_u16 =
            {
                {
                    vget_low_u16(vmovl_u8(vget_low_u8(b20_u8))),
                    vget_high_u16(vmovl_u8(vget_low_u8(b20_u8))),
                    vget_low_u16(vmovl_u8(vget_high_u8(b20_u8))),
                    vget_high_u16(vmovl_u8(vget_high_u8(b20_u8)))
                }
            };

            const uint16x4x4_t b30_u16 =
            {
                {
                    vget_low_u16(vmovl_u8(vget_low_u8(b30_u8))),
                    vget_high_u16(vmovl_u8(vget_low_u8(b30_u8))),
                    vget_low_u16(vmovl_u8(vget_high_u8(b30_u8))),
                    vget_high_u16(vmovl_u8(vget_high_u8(b30_u8)))
                }
            };

            const uint16x4x4_t b40_u16 =
            {
                {
                    vget_low_u16(vmovl_u8(vget_low_u8(b40_u8))),
                    vget_high_u16(vmovl_u8(vget_low_u8(b40_u8))),
                    vget_low_u16(vmovl_u8(vget_high_u8(b40_u8))),
                    vget_high_u16(vmovl_u8(vget_high_u8(b40_u8)))
                }
            };

            const uint16x4x4_t b50_u16 =
            {
                {
                    vget_low_u16(vmovl_u8(vget_low_u8(b50_u8))),
                    vget_high_u16(vmovl_u8(vget_low_u8(b50_u8))),
                    vget_low_u16(vmovl_u8(vget_high_u8(b50_u8))),
                    vget_high_u16(vmovl_u8(vget_high_u8(b50_u8)))
                }
            };

            const uint16x4x4_t b60_u16 =
            {
                {
                    vget_low_u16(vmovl_u8(vget_low_u8(b60_u8))),
                    vget_high_u16(vmovl_u8(vget_low_u8(b60_u8))),
                    vget_low_u16(vmovl_u8(vget_high_u8(b60_u8))),
                    vget_high_u16(vmovl_u8(vget_high_u8(b60_u8)))
                }
            };

            const uint16x4x4_t b70_u16 =
            {
                {
                    vget_low_u16(vmovl_u8(vget_low_u8(b70_u8))),
                    vget_high_u16(vmovl_u8(vget_low_u8(b70_u8))),
                    vget_low_u16(vmovl_u8(vget_high_u8(b70_u8))),
                    vget_high_u16(vmovl_u8(vget_high_u8(b70_u8)))
                }
            };

            // Accumulate 0:
            c0.val[0] = vmlal_lane_u16(c0.val[0], b00_u16.val[0], a00_u16.val[0], 0);
            c0.val[1] = vmlal_lane_u16(c0.val[1], b00_u16.val[1], a00_u16.val[0], 0);
            c0.val[2] = vmlal_lane_u16(c0.val[2], b00_u16.val[2], a00_u16.val[0], 0);
            c0.val[3] = vmlal_lane_u16(c0.val[3], b00_u16.val[3], a00_u16.val[0], 0);

            // Accumulate 1:
            c0.val[0] = vmlal_lane_u16(c0.val[0], b10_u16.val[0], a00_u16.val[0], 1);
            c0.val[1] = vmlal_lane_u16(c0.val[1], b10_u16.val[1], a00_u16.val[0], 1);
            c0.val[2] = vmlal_lane_u16(c0.val[2], b10_u16.val[2], a00_u16.val[0], 1);
            c0.val[3] = vmlal_lane_u16(c0.val[3], b10_u16.val[3], a00_u16.val[0], 1);

            // Accumulate 2:
            c0.val[0] = vmlal_lane_u16(c0.val[0], b20_u16.val[0], a00_u16.val[0], 2);
            c0.val[1] = vmlal_lane_u16(c0.val[1], b20_u16.val[1], a00_u16.val[0], 2);
            c0.val[2] = vmlal_lane_u16(c0.val[2], b20_u16.val[2], a00_u16.val[0], 2);
            c0.val[3] = vmlal_lane_u16(c0.val[3], b20_u16.val[3], a00_u16.val[0], 2);

            // Accumulate 3:
            c0.val[0] = vmlal_lane_u16(c0.val[0], b30_u16.val[0], a00_u16.val[0], 3);
            c0.val[1] = vmlal_lane_u16(c0.val[1], b30_u16.val[1], a00_u16.val[0], 3);
            c0.val[2] = vmlal_lane_u16(c0.val[2], b30_u16.val[2], a00_u16.val[0], 3);
            c0.val[3] = vmlal_lane_u16(c0.val[3], b30_u16.val[3], a00_u16.val[0], 3);

            // Accumulate 4:
            c0.val[0] = vmlal_lane_u16(c0.val[0], b40_u16.val[0], a00_u16.val[1], 0);
            c0.val[1] = vmlal_lane_u16(c0.val[1], b40_u16.val[1], a00_u16.val[1], 0);
            c0.val[2] = vmlal_lane_u16(c0.val[2], b40_u16.val[2], a00_u16.val[1], 0);
            c0.val[3] = vmlal_lane_u16(c0.val[3], b40_u16.val[3], a00_u16.val[1], 0);

            // Accumulate 5:
            c0.val[0] = vmlal_lane_u16(c0.val[0], b50_u16.val[0], a00_u16.val[1], 1);
            c0.val[1] = vmlal_lane_u16(c0.val[1], b50_u16.val[1], a00_u16.val[1], 1);
            c0.val[2] = vmlal_lane_u16(c0.val[2], b50_u16.val[2], a00_u16.val[1], 1);
            c0.val[3] = vmlal_lane_u16(c0.val[3], b50_u16.val[3], a00_u16.val[1], 1);

            // Accumulate 6:
            c0.val[0] = vmlal_lane_u16(c0.val[0], b60_u16.val[0], a00_u16.val[1], 2);
            c0.val[1] = vmlal_lane_u16(c0.val[1], b60_u16.val[1], a00_u16.val[1], 2);
            c0.val[2] = vmlal_lane_u16(c0.val[2], b60_u16.val[2], a00_u16.val[1], 2);
            c0.val[3] = vmlal_lane_u16(c0.val[3], b60_u16.val[3], a00_u16.val[1], 2);

            // Accumulate 7:
            c0.val[0] = vmlal_lane_u16(c0.val[0], b70_u16.val[0], a00_u16.val[1], 3);
            c0.val[1] = vmlal_lane_u16(c0.val[1], b70_u16.val[1], a00_u16.val[1], 3);
            c0.val[2] = vmlal_lane_u16(c0.val[2], b70_u16.val[2], a00_u16.val[1], 3);
            c0.val[3] = vmlal_lane_u16(c0.val[3], b70_u16.val[3], a00_u16.val[1], 3);

            vec_a += 8;
            matrix_b += 8 * stride_b;
        }

        // This for loop performs the left-over accumulations
        for(; vec_a < vec_a_end_addr;)
        {
            const uint8x8_t  a00_u8 = vld1_dup_u8(vec_a);
            const uint8x16_t b00_u8 = vld1q_u8(matrix_b);

            const uint16x4x4_t b00_u16 =
            {
                {
                    vget_low_u16(vmovl_u8(vget_low_u8(b00_u8))),
                    vget_high_u16(vmovl_u8(vget_low_u8(b00_u8))),
                    vget_low_u16(vmovl_u8(vget_high_u8(b00_u8))),
                    vget_high_u16(vmovl_u8(vget_high_u8(b00_u8)))
                }
            };

            // Convert a00_u8 to uint16_t and get the lower part
            const uint16x4_t a00_u16 = vget_low_u16(vmovl_u8(a00_u8));

            // Accumulate 0:
            c0.val[0] = vmlal_lane_u16(c0.val[0], b00_u16.val[0], a00_u16, 0);
            c0.val[1] = vmlal_lane_u16(c0.val[1], b00_u16.val[1], a00_u16, 0);
            c0.val[2] = vmlal_lane_u16(c0.val[2], b00_u16.val[2], a00_u16, 0);
            c0.val[3] = vmlal_lane_u16(c0.val[3], b00_u16.val[3], a00_u16, 0);

            vec_a += 1;
            matrix_b += stride_b;
        }

        auto vec_out = reinterpret_cast<int32_t *>(out.ptr());
        vst1q_s32(vec_out + 0, vreinterpretq_s32_u32(c0.val[0]));
        vst1q_s32(vec_out + 4, vreinterpretq_s32_u32(c0.val[1]));
        vst1q_s32(vec_out + 8, vreinterpretq_s32_u32(c0.val[2]));
        vst1q_s32(vec_out + 12, vreinterpretq_s32_u32(c0.val[3]));
    },
    ina, inb, out);
}

void inline vector_matrix_multiply_s8(Iterator &ina, Iterator &inb, Iterator &out, int width_a, int width_b, size_t stride_b, const Window &window)
{
    execute_window_loop(window, [&](const Coordinates & id)
    {
        if(id.x() > width_b)
        {
            return;
        }

        // Accumulators for the block 0
        int32x4x4_t c0 =
        {
            {
                vdupq_n_s32(0),
                vdupq_n_s32(0),
                vdupq_n_s32(0),
                vdupq_n_s32(0)
            }
        };

        auto vec_a          = reinterpret_cast<const int8_t *>(ina.ptr());
        auto matrix_b       = reinterpret_cast<const int8_t *>(inb.ptr());
        auto vec_a_end_addr = vec_a + width_a;

        // This for loop performs 8 accumulations
        for(; vec_a <= (vec_a_end_addr - 8);)
        {
            const int8x8_t  a00_s8 = vld1_s8(vec_a);
            const int8x16_t b00_s8 = vld1q_s8(matrix_b + 0 * stride_b);
            const int8x16_t b10_s8 = vld1q_s8(matrix_b + 1 * stride_b);
            const int8x16_t b20_s8 = vld1q_s8(matrix_b + 2 * stride_b);
            const int8x16_t b30_s8 = vld1q_s8(matrix_b + 3 * stride_b);
            const int8x16_t b40_s8 = vld1q_s8(matrix_b + 4 * stride_b);
            const int8x16_t b50_s8 = vld1q_s8(matrix_b + 5 * stride_b);
            const int8x16_t b60_s8 = vld1q_s8(matrix_b + 6 * stride_b);
            const int8x16_t b70_s8 = vld1q_s8(matrix_b + 7 * stride_b);

            // Convert a00_s8 to int16_t and get the lower part
            const int16x4x2_t a00_s16 =
            {
                {
                    vget_low_s16(vmovl_s8(a00_s8)),
                    vget_high_s16(vmovl_s8(a00_s8))
                }
            };

            const int16x4x4_t b00_s16 =
            {
                {
                    vget_low_s16(vmovl_s8(vget_low_s8(b00_s8))),
                    vget_high_s16(vmovl_s8(vget_low_s8(b00_s8))),
                    vget_low_s16(vmovl_s8(vget_high_s8(b00_s8))),
                    vget_high_s16(vmovl_s8(vget_high_s8(b00_s8)))
                }
            };

            const int16x4x4_t b10_s16 =
            {
                {
                    vget_low_s16(vmovl_s8(vget_low_s8(b10_s8))),
                    vget_high_s16(vmovl_s8(vget_low_s8(b10_s8))),
                    vget_low_s16(vmovl_s8(vget_high_s8(b10_s8))),
                    vget_high_s16(vmovl_s8(vget_high_s8(b10_s8)))
                }
            };

            const int16x4x4_t b20_s16 =
            {
                {
                    vget_low_s16(vmovl_s8(vget_low_s8(b20_s8))),
                    vget_high_s16(vmovl_s8(vget_low_s8(b20_s8))),
                    vget_low_s16(vmovl_s8(vget_high_s8(b20_s8))),
                    vget_high_s16(vmovl_s8(vget_high_s8(b20_s8)))
                }
            };

            const int16x4x4_t b30_s16 =
            {
                {
                    vget_low_s16(vmovl_s8(vget_low_s8(b30_s8))),
                    vget_high_s16(vmovl_s8(vget_low_s8(b30_s8))),
                    vget_low_s16(vmovl_s8(vget_high_s8(b30_s8))),
                    vget_high_s16(vmovl_s8(vget_high_s8(b30_s8)))
                }
            };

            const int16x4x4_t b40_s16 =
            {
                {
                    vget_low_s16(vmovl_s8(vget_low_s8(b40_s8))),
                    vget_high_s16(vmovl_s8(vget_low_s8(b40_s8))),
                    vget_low_s16(vmovl_s8(vget_high_s8(b40_s8))),
                    vget_high_s16(vmovl_s8(vget_high_s8(b40_s8)))
                }
            };

            const int16x4x4_t b50_s16 =
            {
                {
                    vget_low_s16(vmovl_s8(vget_low_s8(b50_s8))),
                    vget_high_s16(vmovl_s8(vget_low_s8(b50_s8))),
                    vget_low_s16(vmovl_s8(vget_high_s8(b50_s8))),
                    vget_high_s16(vmovl_s8(vget_high_s8(b50_s8)))
                }
            };

            const int16x4x4_t b60_s16 =
            {
                {
                    vget_low_s16(vmovl_s8(vget_low_s8(b60_s8))),
                    vget_high_s16(vmovl_s8(vget_low_s8(b60_s8))),
                    vget_low_s16(vmovl_s8(vget_high_s8(b60_s8))),
                    vget_high_s16(vmovl_s8(vget_high_s8(b60_s8)))
                }
            };

            const int16x4x4_t b70_s16 =
            {
                {
                    vget_low_s16(vmovl_s8(vget_low_s8(b70_s8))),
                    vget_high_s16(vmovl_s8(vget_low_s8(b70_s8))),
                    vget_low_s16(vmovl_s8(vget_high_s8(b70_s8))),
                    vget_high_s16(vmovl_s8(vget_high_s8(b70_s8)))
                }
            };

            // Accumulate 0:
            c0.val[0] = vmlal_lane_s16(c0.val[0], b00_s16.val[0], a00_s16.val[0], 0);
            c0.val[1] = vmlal_lane_s16(c0.val[1], b00_s16.val[1], a00_s16.val[0], 0);
            c0.val[2] = vmlal_lane_s16(c0.val[2], b00_s16.val[2], a00_s16.val[0], 0);
            c0.val[3] = vmlal_lane_s16(c0.val[3], b00_s16.val[3], a00_s16.val[0], 0);

            // Accumulate 1:
            c0.val[0] = vmlal_lane_s16(c0.val[0], b10_s16.val[0], a00_s16.val[0], 1);
            c0.val[1] = vmlal_lane_s16(c0.val[1], b10_s16.val[1], a00_s16.val[0], 1);
            c0.val[2] = vmlal_lane_s16(c0.val[2], b10_s16.val[2], a00_s16.val[0], 1);
            c0.val[3] = vmlal_lane_s16(c0.val[3], b10_s16.val[3], a00_s16.val[0], 1);

            // Accumulate 2:
            c0.val[0] = vmlal_lane_s16(c0.val[0], b20_s16.val[0], a00_s16.val[0], 2);
            c0.val[1] = vmlal_lane_s16(c0.val[1], b20_s16.val[1], a00_s16.val[0], 2);
            c0.val[2] = vmlal_lane_s16(c0.val[2], b20_s16.val[2], a00_s16.val[0], 2);
            c0.val[3] = vmlal_lane_s16(c0.val[3], b20_s16.val[3], a00_s16.val[0], 2);

            // Accumulate 3:
            c0.val[0] = vmlal_lane_s16(c0.val[0], b30_s16.val[0], a00_s16.val[0], 3);
            c0.val[1] = vmlal_lane_s16(c0.val[1], b30_s16.val[1], a00_s16.val[0], 3);
            c0.val[2] = vmlal_lane_s16(c0.val[2], b30_s16.val[2], a00_s16.val[0], 3);
            c0.val[3] = vmlal_lane_s16(c0.val[3], b30_s16.val[3], a00_s16.val[0], 3);

            // Accumulate 4:
            c0.val[0] = vmlal_lane_s16(c0.val[0], b40_s16.val[0], a00_s16.val[1], 0);
            c0.val[1] = vmlal_lane_s16(c0.val[1], b40_s16.val[1], a00_s16.val[1], 0);
            c0.val[2] = vmlal_lane_s16(c0.val[2], b40_s16.val[2], a00_s16.val[1], 0);
            c0.val[3] = vmlal_lane_s16(c0.val[3], b40_s16.val[3], a00_s16.val[1], 0);

            // Accumulate 5:
            c0.val[0] = vmlal_lane_s16(c0.val[0], b50_s16.val[0], a00_s16.val[1], 1);
            c0.val[1] = vmlal_lane_s16(c0.val[1], b50_s16.val[1], a00_s16.val[1], 1);
            c0.val[2] = vmlal_lane_s16(c0.val[2], b50_s16.val[2], a00_s16.val[1], 1);
            c0.val[3] = vmlal_lane_s16(c0.val[3], b50_s16.val[3], a00_s16.val[1], 1);

            // Accumulate 6:
            c0.val[0] = vmlal_lane_s16(c0.val[0], b60_s16.val[0], a00_s16.val[1], 2);
            c0.val[1] = vmlal_lane_s16(c0.val[1], b60_s16.val[1], a00_s16.val[1], 2);
            c0.val[2] = vmlal_lane_s16(c0.val[2], b60_s16.val[2], a00_s16.val[1], 2);
            c0.val[3] = vmlal_lane_s16(c0.val[3], b60_s16.val[3], a00_s16.val[1], 2);

            // Accumulate 7:
            c0.val[0] = vmlal_lane_s16(c0.val[0], b70_s16.val[0], a00_s16.val[1], 3);
            c0.val[1] = vmlal_lane_s16(c0.val[1], b70_s16.val[1], a00_s16.val[1], 3);
            c0.val[2] = vmlal_lane_s16(c0.val[2], b70_s16.val[2], a00_s16.val[1], 3);
            c0.val[3] = vmlal_lane_s16(c0.val[3], b70_s16.val[3], a00_s16.val[1], 3);

            vec_a += 8;
            matrix_b += 8 * stride_b;
        }

        // This for loop performs the left-over accumulations
        for(; vec_a < vec_a_end_addr;)
        {
            const int8x8_t  a00_s8 = vld1_dup_s8(vec_a);
            const int8x16_t b00_s8 = vld1q_s8(matrix_b);

            const int16x4x4_t b00_s16 =
            {
                {
                    vget_low_s16(vmovl_s8(vget_low_s8(b00_s8))),
                    vget_high_s16(vmovl_s8(vget_low_s8(b00_s8))),
                    vget_low_s16(vmovl_s8(vget_high_s8(b00_s8))),
                    vget_high_s16(vmovl_s8(vget_high_s8(b00_s8)))
                }
            };

            // Convert a00_s8 to uint16_t and get the lower part
            const int16x4_t a00_s16 = vget_low_s16(vmovl_s8(a00_s8));

            // Accumulate 0:
            c0.val[0] = vmlal_lane_s16(c0.val[0], b00_s16.val[0], a00_s16, 0);
            c0.val[1] = vmlal_lane_s16(c0.val[1], b00_s16.val[1], a00_s16, 0);
            c0.val[2] = vmlal_lane_s16(c0.val[2], b00_s16.val[2], a00_s16, 0);
            c0.val[3] = vmlal_lane_s16(c0.val[3], b00_s16.val[3], a00_s16, 0);

            vec_a += 1;
            matrix_b += stride_b;
        }

        auto vec_out = reinterpret_cast<int32_t *>(out.ptr());
        vst1q_s32(vec_out + 0, c0.val[0]);
        vst1q_s32(vec_out + 4, c0.val[1]);
        vst1q_s32(vec_out + 8, c0.val[2]);
        vst1q_s32(vec_out + 12, c0.val[3]);
    },
    ina, inb, out);
}

void inline matrix_multiply_u8(Iterator &ina, Iterator &inb, Iterator &out, int width_b, size_t out_stride, const Window &window)
{
    execute_window_loop(window, [&](const Coordinates &)
    {
        const uint8_t *mtx_a0 = ina.ptr();
        const uint8_t *mtx_b0 = inb.ptr();

        // Note: Since the input are all positives, we can use uint32_t
        // Accumulators for the block 0
        uint32x4x4_t c0 =
        {
            {
                vdupq_n_u32(0),
                vdupq_n_u32(0),
                vdupq_n_u32(0),
                vdupq_n_u32(0)
            }
        };

        // Accumulators for the block 1
        uint32x4x4_t c1 =
        {
            {
                vdupq_n_u32(0),
                vdupq_n_u32(0),
                vdupq_n_u32(0),
                vdupq_n_u32(0)
            }
        };

        // Accumulators for the block 2
        uint32x4x4_t c2 =
        {
            {
                vdupq_n_u32(0),
                vdupq_n_u32(0),
                vdupq_n_u32(0),
                vdupq_n_u32(0)
            }
        };

        // Accumulators for the block 3
        uint32x4x4_t c3 =
        {
            {
                vdupq_n_u32(0),
                vdupq_n_u32(0),
                vdupq_n_u32(0),
                vdupq_n_u32(0)
            }
        };

        for(int k = 0; k < width_b; k += 16, mtx_a0 += 4, mtx_b0 += 16)
        {
            const uint8x8_t  a00_u8 = vld1_u8(mtx_a0);
            const uint8x16_t b00_u8 = vld1q_u8(mtx_b0);

            // Convert a00_u8 to uint16_t and get the lower part
            const uint16x4_t a00_u16 = vget_low_u16(vmovl_u8(a00_u8));

            // Convert b00_s8 to uint16_t
            const uint16x4x4_t b00_u16 =
            {
                {
                    vget_low_u16(vmovl_u8(vget_low_u8(b00_u8))),
                    vget_high_u16(vmovl_u8(vget_low_u8(b00_u8))),
                    vget_low_u16(vmovl_u8(vget_high_u8(b00_u8))),
                    vget_high_u16(vmovl_u8(vget_high_u8(b00_u8)))
                }
            };

            // 4x4 block 0
            c0.val[0] = vmlal_lane_u16(c0.val[0], b00_u16.val[0], a00_u16, 0);
            c0.val[1] = vmlal_lane_u16(c0.val[1], b00_u16.val[1], a00_u16, 0);
            c0.val[2] = vmlal_lane_u16(c0.val[2], b00_u16.val[2], a00_u16, 0);
            c0.val[3] = vmlal_lane_u16(c0.val[3], b00_u16.val[3], a00_u16, 0);

            // 4x4 block 1
            c1.val[0] = vmlal_lane_u16(c1.val[0], b00_u16.val[0], a00_u16, 1);
            c1.val[1] = vmlal_lane_u16(c1.val[1], b00_u16.val[1], a00_u16, 1);
            c1.val[2] = vmlal_lane_u16(c1.val[2], b00_u16.val[2], a00_u16, 1);
            c1.val[3] = vmlal_lane_u16(c1.val[3], b00_u16.val[3], a00_u16, 1);

            // 4x4 block 2
            c2.val[0] = vmlal_lane_u16(c2.val[0], b00_u16.val[0], a00_u16, 2);
            c2.val[1] = vmlal_lane_u16(c2.val[1], b00_u16.val[1], a00_u16, 2);
            c2.val[2] = vmlal_lane_u16(c2.val[2], b00_u16.val[2], a00_u16, 2);
            c2.val[3] = vmlal_lane_u16(c2.val[3], b00_u16.val[3], a00_u16, 2);

            // 4x4 block 3
            c3.val[0] = vmlal_lane_u16(c3.val[0], b00_u16.val[0], a00_u16, 3);
            c3.val[1] = vmlal_lane_u16(c3.val[1], b00_u16.val[1], a00_u16, 3);
            c3.val[2] = vmlal_lane_u16(c3.val[2], b00_u16.val[2], a00_u16, 3);
            c3.val[3] = vmlal_lane_u16(c3.val[3], b00_u16.val[3], a00_u16, 3);
        }

        auto mtx_out = reinterpret_cast<int32_t *>(out.ptr());
        vst1q_s32(mtx_out + 0 * out_stride + 0, vreinterpretq_s32_u32(c0.val[0]));
        vst1q_s32(mtx_out + 0 * out_stride + 4, vreinterpretq_s32_u32(c0.val[1]));
        vst1q_s32(mtx_out + 0 * out_stride + 8, vreinterpretq_s32_u32(c0.val[2]));
        vst1q_s32(mtx_out + 0 * out_stride + 12, vreinterpretq_s32_u32(c0.val[3]));
        vst1q_s32(mtx_out + 1 * out_stride + 0, vreinterpretq_s32_u32(c1.val[0]));
        vst1q_s32(mtx_out + 1 * out_stride + 4, vreinterpretq_s32_u32(c1.val[1]));
        vst1q_s32(mtx_out + 1 * out_stride + 8, vreinterpretq_s32_u32(c1.val[2]));
        vst1q_s32(mtx_out + 1 * out_stride + 12, vreinterpretq_s32_u32(c1.val[3]));
        vst1q_s32(mtx_out + 2 * out_stride + 0, vreinterpretq_s32_u32(c2.val[0]));
        vst1q_s32(mtx_out + 2 * out_stride + 4, vreinterpretq_s32_u32(c2.val[1]));
        vst1q_s32(mtx_out + 2 * out_stride + 8, vreinterpretq_s32_u32(c2.val[2]));
        vst1q_s32(mtx_out + 2 * out_stride + 12, vreinterpretq_s32_u32(c2.val[3]));
        vst1q_s32(mtx_out + 3 * out_stride + 0, vreinterpretq_s32_u32(c3.val[0]));
        vst1q_s32(mtx_out + 3 * out_stride + 4, vreinterpretq_s32_u32(c3.val[1]));
        vst1q_s32(mtx_out + 3 * out_stride + 8, vreinterpretq_s32_u32(c3.val[2]));
        vst1q_s32(mtx_out + 3 * out_stride + 12, vreinterpretq_s32_u32(c3.val[3]));
    },
    ina, inb, out);
}

void inline matrix_multiply_s8(Iterator &ina, Iterator &inb, Iterator &out, int width_b, size_t out_stride, const Window &window)
{
    // The implementation assumes that the matrix A and Matrix B have been reshaped respectively with NEGEMMInterleave4x4 and NEGEMMTranspose1xW
    // The reshaping of the matrices helps to have a cache friendly implementation and helps to avoid the data re-arrangements needed for computing 16x4 elements per iteration
    // All the values needed for computing a single 4x4 block will be read from consecutive memory positions
    execute_window_loop(window, [&](const Coordinates &)
    {
        auto *mtx_a0 = reinterpret_cast<const int8_t *>(ina.ptr());
        auto *mtx_b0 = reinterpret_cast<const int8_t *>(inb.ptr());

        // Note: Since the input are all positives, we can use uint32_t
        // Accumulators for the block 0
        int32x4x4_t c0 =
        {
            {
                vdupq_n_s32(0),
                vdupq_n_s32(0),
                vdupq_n_s32(0),
                vdupq_n_s32(0)
            }
        };

        // Accumulators for the block 1
        int32x4x4_t c1 =
        {
            {
                vdupq_n_s32(0),
                vdupq_n_s32(0),
                vdupq_n_s32(0),
                vdupq_n_s32(0)
            }
        };

        // Accumulators for the block 2
        int32x4x4_t c2 =
        {
            {
                vdupq_n_s32(0),
                vdupq_n_s32(0),
                vdupq_n_s32(0),
                vdupq_n_s32(0)
            }
        };

        // Accumulators for the block 3
        int32x4x4_t c3 =
        {
            {
                vdupq_n_s32(0),
                vdupq_n_s32(0),
                vdupq_n_s32(0),
                vdupq_n_s32(0)
            }
        };

        for(int k = 0; k < width_b; k += 16, mtx_a0 += 4, mtx_b0 += 16)
        {
            const int8x8_t  a00_s8 = vld1_s8(mtx_a0);
            const int8x16_t b00_s8 = vld1q_s8(mtx_b0);

            // Convert a00_s8 to uint16_t and get the lower part
            const int16x4_t a00_s16 = vget_low_s16(vmovl_s8(a00_s8));

            // Convert b00_s8 to int16_t
            const int16x4x4_t b00_s16 =
            {
                {
                    vget_low_s16(vmovl_s8(vget_low_s8(b00_s8))),
                    vget_high_s16(vmovl_s8(vget_low_s8(b00_s8))),
                    vget_low_s16(vmovl_s8(vget_high_s8(b00_s8))),
                    vget_high_s16(vmovl_s8(vget_high_s8(b00_s8)))
                }
            };

            // 4x4 block 0
            c0.val[0] = vmlal_lane_s16(c0.val[0], b00_s16.val[0], a00_s16, 0);
            c0.val[1] = vmlal_lane_s16(c0.val[1], b00_s16.val[1], a00_s16, 0);
            c0.val[2] = vmlal_lane_s16(c0.val[2], b00_s16.val[2], a00_s16, 0);
            c0.val[3] = vmlal_lane_s16(c0.val[3], b00_s16.val[3], a00_s16, 0);

            // 4x4 block 1
            c1.val[0] = vmlal_lane_s16(c1.val[0], b00_s16.val[0], a00_s16, 1);
            c1.val[1] = vmlal_lane_s16(c1.val[1], b00_s16.val[1], a00_s16, 1);
            c1.val[2] = vmlal_lane_s16(c1.val[2], b00_s16.val[2], a00_s16, 1);
            c1.val[3] = vmlal_lane_s16(c1.val[3], b00_s16.val[3], a00_s16, 1);

            // 4x4 block 2
            c2.val[0] = vmlal_lane_s16(c2.val[0], b00_s16.val[0], a00_s16, 2);
            c2.val[1] = vmlal_lane_s16(c2.val[1], b00_s16.val[1], a00_s16, 2);
            c2.val[2] = vmlal_lane_s16(c2.val[2], b00_s16.val[2], a00_s16, 2);
            c2.val[3] = vmlal_lane_s16(c2.val[3], b00_s16.val[3], a00_s16, 2);

            // 4x4 block 3
            c3.val[0] = vmlal_lane_s16(c3.val[0], b00_s16.val[0], a00_s16, 3);
            c3.val[1] = vmlal_lane_s16(c3.val[1], b00_s16.val[1], a00_s16, 3);
            c3.val[2] = vmlal_lane_s16(c3.val[2], b00_s16.val[2], a00_s16, 3);
            c3.val[3] = vmlal_lane_s16(c3.val[3], b00_s16.val[3], a00_s16, 3);
        }

        auto mtx_out = reinterpret_cast<int32_t *>(out.ptr());
        vst1q_s32(mtx_out + 0 * out_stride + 0, c0.val[0]);
        vst1q_s32(mtx_out + 0 * out_stride + 4, c0.val[1]);
        vst1q_s32(mtx_out + 0 * out_stride + 8, c0.val[2]);
        vst1q_s32(mtx_out + 0 * out_stride + 12, c0.val[3]);
        vst1q_s32(mtx_out + 1 * out_stride + 0, c1.val[0]);
        vst1q_s32(mtx_out + 1 * out_stride + 4, c1.val[1]);
        vst1q_s32(mtx_out + 1 * out_stride + 8, c1.val[2]);
        vst1q_s32(mtx_out + 1 * out_stride + 12, c1.val[3]);
        vst1q_s32(mtx_out + 2 * out_stride + 0, c2.val[0]);
        vst1q_s32(mtx_out + 2 * out_stride + 4, c2.val[1]);
        vst1q_s32(mtx_out + 2 * out_stride + 8, c2.val[2]);
        vst1q_s32(mtx_out + 2 * out_stride + 12, c2.val[3]);
        vst1q_s32(mtx_out + 3 * out_stride + 0, c3.val[0]);
        vst1q_s32(mtx_out + 3 * out_stride + 4, c3.val[1]);
        vst1q_s32(mtx_out + 3 * out_stride + 8, c3.val[2]);
        vst1q_s32(mtx_out + 3 * out_stride + 12, c3.val[3]);
    },
    ina, inb, out);
}
} // namespace

class Coordinates;
} // namespace arm_compute

namespace
{
Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output)
{
    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::S8, DataType::U8);
    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::QSYMM8_PER_CHANNEL, DataType::S8, DataType::U8);
    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::S32);

    TensorShape in0_shape = input0->tensor_shape();
    TensorShape in1_shape = input1->tensor_shape();
    TensorShape out_shape = output->tensor_shape();

    // Check vector-by-matrix case
    if(out_shape[1] == 1)
    {
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(in0_shape[0] != in1_shape[1], "The number of input0's columns must be equal to input1's rows");
    }
    else
    {
        in0_shape.collapse(2);
        in1_shape.collapse(2);
        out_shape.collapse(2);

        ARM_COMPUTE_RETURN_ERROR_ON_MSG(in0_shape[2] != out_shape[2], "Output tensor must have the same number of batches of input0 tensor");
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(in1_shape[2] != 1 && in0_shape[2] != in1_shape[2], "Input1 tensor must have the same number of batches of input0 or the number of batches must be set to 1");
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(in1_shape[0] % 16, "Input1's width must be a multiple of 16");
    }

    return Status{};
}

std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITensorInfo *input1, ITensorInfo *output)
{
    constexpr unsigned int num_elems_processed_per_iteration_x = 16;
    constexpr unsigned int num_elems_processed_per_iteration_y = 4;

    Window win;
    bool   window_changed = false;

    // Check if the output tensor is a vector. If so,the kernel runs the vector-matrix multiplication
    if((output->dimension(1) == 1))
    {
        // Configure kernel window
        win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration_x));

        // We cannot read out-of-bound elements from matrix A as we use the left-over for loop
        AccessWindowStatic     in0_access(input0, 0, 0, input0->tensor_shape().x(), 1);
        AccessWindowHorizontal in1_access(input1, 0, num_elems_processed_per_iteration_x);
        AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration_x);

        window_changed = update_window_and_padding(win, in0_access, in1_access, output_access);

        Coordinates coord;
        coord.set_num_dimensions(output->num_dimensions());
        output_access.set_valid_region(win, ValidRegion(coord, output->tensor_shape()));
    }
    else
    {
        win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));

        unsigned int num_k_iterations = ceil_to_multiple(input1->dimension(0), num_elems_processed_per_iteration_x) / 16;
        // For each iteration of "k" we increment the input pointer by 4, and we load 8 elements a the time:
        AccessWindowStatic    in0_access(input0, 0, 0, (num_k_iterations - 1) * 4 + 8, input0->dimension(1));
        AccessWindowStatic    in1_access(input1, 0, 0, ceil_to_multiple(input1->dimension(0), num_elems_processed_per_iteration_x), input1->dimension(1));
        AccessWindowRectangle output_access(output, 0, 0, num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y);

        window_changed = update_window_and_padding(win, in0_access, in1_access, output_access);

        output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape()));
    }

    Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
    return std::make_pair(err, win);
}
} // namespace

NEGEMMLowpMatrixMultiplyKernel::NEGEMMLowpMatrixMultiplyKernel()
    : _input0(nullptr), _input1(nullptr), _output(nullptr), _slide_matrix_b(true)
{
}

void NEGEMMLowpMatrixMultiplyKernel::configure(const ITensor *input0, const ITensor *input1, ITensor *output)
{
    ARM_COMPUTE_ERROR_ON_NULLPTR(input0, input1, output);
    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input0->info(), input1->info(), output->info()));

    TensorShape in1_shape = input1->info()->tensor_shape();
    in1_shape.collapse(2);

    _input0         = input0;
    _input1         = input1;
    _output         = output;
    _slide_matrix_b = in1_shape[2] != 1;

    // Configure kernel window
    auto win_config = validate_and_configure_window(input0->info(), input1->info(), output->info());
    ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
    INEKernel::configure(win_config.second);
}

Status NEGEMMLowpMatrixMultiplyKernel::validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output)
{
    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input0, input1, output));
    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input0->clone().get(), input1->clone().get(), output->clone().get()).first);

    return Status{};
}

void NEGEMMLowpMatrixMultiplyKernel::run(const Window &window, const ThreadInfo &info)
{
    ARM_COMPUTE_UNUSED(info);
    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);

    // Check if the output tensor is a vector. If so,the kernel runs the vector-matrix multiplication path
    if((_output->info()->dimension(1) == 1))
    {
        const auto width_matrix_a = static_cast<int>(_input0->info()->dimension(0));
        const auto width_matrix_b = static_cast<int>(_input1->info()->dimension(0));
        const auto in_b_stride    = static_cast<int>(_input1->info()->strides_in_bytes()[1] / data_size_from_type(_input1->info()->data_type()));

        // The implementation computes 16 elements per iteration
        const int window_start_x = 16 * info.thread_id;
        const int window_step_x  = 16 * info.num_threads;
        // Make sure (window_end_x - window_start_x) is a multiple of window_step_x
        const int window_end_x = ceil_to_multiple(width_matrix_b - window_start_x, window_step_x) + window_start_x;

        Window win_out(window);
        win_out.set(Window::DimX, Window::Dimension(window_start_x, window_end_x, window_step_x));
        win_out.set(Window::DimY, Window::Dimension(0, 1, 1));

        Window win_a(window);
        win_a.set(Window::DimX, Window::Dimension(0, 0, 0));
        win_a.set(Window::DimY, Window::Dimension(0, 0, 0));

        Window win_b;
        // Don't slice matrix B along the z dimension if matrix B has just 2 dimensions and matrix A more than 2
        // This scenario can happen when the the matrix multiplication is used to perform a convolution operation
        if(_input1->info()->num_dimensions() >= 3)
        {
            win_b = window;
        }
        win_b.set(Window::DimX, Window::Dimension(window_start_x, window_end_x, window_step_x));
        win_b.set(Window::DimY, Window::Dimension(0, 1, 1));

        Iterator ina(_input0, win_a);
        Iterator inb(_input1, win_b);
        Iterator out(_output, win_out);

        switch(_input0->info()->data_type())
        {
            case DataType::S8:
            {
                vector_matrix_multiply_s8(ina, inb, out, width_matrix_a, width_matrix_b, in_b_stride, window);
                break;
            }
            case DataType::U8:
            case DataType::QASYMM8:
            {
                vector_matrix_multiply_u8(ina, inb, out, width_matrix_a, width_matrix_b, in_b_stride, window);
                break;
            }
            default:
            {
                ARM_COMPUTE_ERROR("Not supported");
                break;
            }
        }
    }
    else
    {
        const size_t in_b_stride = _input1->info()->strides_in_bytes()[1];
        const size_t out_stride  = _output->info()->strides_in_bytes()[1] / _output->info()->element_size();

        // Set step_x and step_y for matrix A. Scale by a factor of 4 the Y range as the input interleaved matrix A has 4 times less the rows of the output matrix
        Window win_a(window);
        win_a.set(Window::DimX, Window::Dimension(0, 0, 0));
        win_a.set(Window::DimY, Window::Dimension(window.y().start() / 4, window.y().end() / 4, 1));

        // Set step_x and step_y for matrix B. Scale by a factor of 16 the X range as the input transposed matrix A has 16 times less the columns of the output matrix
        Window win_b;
        // Don't slice matrix B along the z dimension if matrix B has just 2 dimensions and matrix A more than 2
        // This scenario can happen when the the matrix multiplication is used to perform a convolution operation
        if(_slide_matrix_b)
        {
            win_b = window;
        }
        win_b.set(Window::DimX, Window::Dimension(window.x().start() / 16, window.x().end() / 16, in_b_stride));
        win_b.set(Window::DimY, Window::Dimension(0, 0, 0));

        // The step x and step y for the output matrix has been already set using in configure()
        Iterator ina(_input0, win_a);
        Iterator inb(_input1, win_b);
        Iterator out(_output, window);

        const int width_b = _input1->info()->dimension(0);
        switch(_input0->info()->data_type())
        {
            case DataType::S8:
            case DataType::QASYMM8_SIGNED:
            {
                matrix_multiply_s8(ina, inb, out, width_b, out_stride, window);
                break;
            }
            case DataType::U8:
            case DataType::QASYMM8:
            {
                matrix_multiply_u8(ina, inb, out, width_b, out_stride, window);
                break;
            }
            default:
            {
                ARM_COMPUTE_ERROR("Not supported");
                break;
            }
        }
    }
}