VCK5000 Versal Development Card for AI Inference - VCK5000 Versal Development Card for AI Inference - 2.5 English - UG1354

Vitis AI Library User Guide (UG1354)
Document_ID
UG1354
Release_Date
2022-06-15
Version
2.5 English

The VCK5000 development card is built on the Xilinx 7 nm Versal ACAP architecture and is designed for designs requiring high throughput AI inference and signal processing compute performance. For this release, DPU core with batch=4, batch=6, and batch=8 are implemented using AI Engines. In theory, each processing engine can provide 10 TOPS INT8 peak performance for deep learning inference, and about 7.5 TOPS INT8 peak performance after the efficiency loss of the AI Engine compiler.

VCK5000 Performance with 4PE 350 MHz DPUCVDX8H

The following table lists the throughput performance (in frames/sec or fps) for various neural network samples on the Versal ACAP VCK5000 Gen3x16 with DPUCVDX8H running at 4PE@350 MHz.

Table 1. VCK5000 Performance with 4PE 350 MHz DPUCVDX8H
No Neural Network Input Size GOPS DPU Frequency (MHz) Performance (fps) (Multiple thread)
1 bcc_pt 800x1000 268.9 350 49.0175
2 centerpoint_0_ptcenterpoint_1_pt 2560x40x4 54 350 146.98
3 densebox_320_320 320x320 0.49 350 3083.38
4 densebox_640_360 360x640 1.1 350 1580.07
5 efficientnet-b0_tf2 224x224 0.36 350 426.838
6 efficientNet-edgetpu-L_tf 300x300 19.36 350 393.635
7 efficientNet-edgetpu-M_tf 240x240 7.34 350 974.597
8 efficientNet-edgetpu-S_tf 224x224 4.72 350 1663.81
9 ENet_cityscapes_pt 512x1024 8.6 350 67.3528
10 face_landmark 96x72 0.14 350 8685.99
11 face-quality 80x60 0.06 350 16646.7
12 face-quality_pt 80x60 0.06 350 16649.7
13 facerec_resnet20 112x96 3.5 350 2956.24
14 facerec_resnet64 112x96 11 350 1509.6
15 facerec-resnet20_mixed_pt 112x96 3.5 350 2954.23
16 facereid-large_pt 96x96 0.5 350 11815.2
17 facereid-small_pt 80x80 0.09 350 17826.5
18 fpn 256x512 8.9 350 393.148
19 FPN_Res18_Medical_segmentation 320x320 45.3 350 153.265
20 FPN-resnet18_covid19-seg_pt 352x352 22.7 350 612.502
21 FPN-resnet18_Endov 240x320 13.75 350 272.666
22 hourglass-pe_mpii 256x256 10.2 350 518.868
23 inception_resnet_v2_tf 299x299 26.4 350 307.795
24 inception_v1 224x224 3.2 350 1507.29
25 inception_v1_tf 224x224 3 350 1585.16
26 inception_v2 224x224 4 350 1254.56
27 inception_v2_tf 224x224 3.88 350 210.043
28 inception_v3 299x299 11.4 350 499.167
29 inception_v3_pt 299x299 5.7 350 494.729
30 inception_v3_tf 299x299 11.5 350 496.163
31 inception_v3_tf2 299x299 11.5 350 490.31
32 inception_v4 299x299 24.5 350 283.49
33 inception_v4_2016_09_09_tf 299x299 24.6 350 280.194
34 medical_seg_cell_tf2 128x128 5.3 350 914.61
35 MLPerf_resnet50_v1.5_tf 224x224 8.19 350 1638.84
36 mlperf_ssd_resnet34_tf 1200x1200 433 350 49.4214
37 mobilenet_1_0_224_tf2 224x224 1.1 350 5864.58
38 mobilenet_edge_0_75_tf 224x224 0.62 350 3344.23
39 mobilenet_edge_1_0_tf 224x224 0.99 350 3101.21
40 mobilenet_v1_0_25_128_tf 128x128 0.027 350 19353.8
41 mobilenet_v1_0_5_160_tf 160x160 0.15 350 13349.2
42 mobilenet_v1_1_0_224_tf 224x224 1.1 350 6561.03
43 mobilenet_v2 224x224 0.6 350 4302.19
44 mobilenet_v2_1_0_224_tf 224x224 0.6 350 4058.7
45 mobilenet_v2_1_4_224_tf 224x224 1.2 350 3137.54
46 mobilenet_v2_cityscapes_tf 1024x2048 132.74 350 16.2875
47 MT-resnet18_mixed_pt 512x320 13.65 350 279.933
48 multi_task 288x512 14.8 350 451.49
49 multi_task_v3_pt 320x512 25.44 350 136.757
50 openpose_pruned_0_3 368x368 49.9 350 87.143
51 personreid-res18_pt 176x80 1.1 350 4782.06
52 personreid-res50_pt 256x128 5.4 350 2012.23
53 plate_detection 320x320 0.49 350 4547.23
54 plate_num 96x288 1.75 350 1541.73
55 pmg_pt 224x224 2.28 350 2000.55
56

pointpainting_nuscenes_pt

 40000x64x16 112 350 20.378
57

pointpillars_kitti_pt

 12000x100x4 10.8 350 3.12967
58

pointpillars_nuscenes_pt

 40000x64x5 108 350 39.0829
59 rcan_pruned_tf 360x640 86.95 350 35.2752
60 refinedet_baseline 480x360 123 350 153.091
61 refinedet_pruned_0_8 360x480 25 350 298.829
62 refinedet_pruned_0_92 360x480 10.1 350 361.42
63 refinedet_pruned_0_96 360x480 5.1 350 376.573
64 refinedet_VOC_tf 320x320 81.9 350 200.903
65 RefineDet-Medical_EDD_tf 320x320 9.8 350 555.168
66 reid 80x160 0.95 350 5045.43
67 resnet_v1_101_tf 224x224 14.4 350 1146.44
68 resnet_v1_152_tf 224x224 21.8 350 825.95
69 resnet_v1_50_tf 224x224 7 350 1817.21
70 resnet_v2_101_tf 299x299 26.78 350 245.955
71 resnet_v2_152_tf 299x299 40.47 350 172.526
72 resnet_v2_50_tf 299x299 13.1 350 427.177
73 resnet18 224x224 3.7 350 2727.74
74 resnet50 224x224 7.7 350 1817.78
75 resnet50_pt 224x224 4.1 350 1675.05
76 resnet50_tf2 224x224 7.7 350 1533.14
77 retinaface 360x640 1.11 350 1621.49
78 SA_gate_base_pt 360x360 187 350 11.2804
79 salsanext_pt 64x2048 20.4 350 108.731
80 salsanext_v2_pt 64x2048 32 350 60.4578
81 semantic_seg_citys_tf2 512x1024 54 350 63.7
82 SemanticFPN_cityscapes_pt 256x512 10 350 730.999
83 SemanticFPN_Mobilenetv2_pt 512x1024 5.4 350 207.713
84 sp_net 128x224 0.55 350 3115.87
85 squeezenet 227x227 0.76 350 3457.99
86 squeezenet_pt 224x224 0.82 350 3375.85
87 ssd_adas_pruned_0_95 360x480 6.3 350 386.074
88 ssd_inception_v2_coco_tf 300x300 9.6 350 105.8
89 ssd_mobilenet_v1_coco_tf 300x300 2.5 350 2286.02
90 ssd_mobilenet_v2 360x480 6.6 350 638.548
91 ssd_mobilenet_v2_coco_tf 300x300 3.8 350 1291.5
92 ssd_pedestrian_pruned_0_97 360x640 5.9 350 317.014
93 ssd_resnet_50_fpn_coco_tf 640x640 178.4 350 80.7027
94 ssd_traffic_pruned_0_9 360x480 11.6 350 379.248
95 ssdlite_mobilenet_v2_coco_tf 300x300 1.5 350 1875.26
96 tiny_yolov3_vmss 416x416 5.46 350 557.548
97 unet_chaos-CT_pt 512x512 23.3 350 128.55
98 vgg_16_tf 224x224 31 350 328.507
99 vgg_19_tf 224x224 39.3 350 293.448
100 vpgnet_pruned_0_99 480x640 2.5 350 232.013
101 yolov2_voc 448x448 34 350 332.582
102 yolov2_voc_pruned_0_66 448x448 11.6 350 411.454
103 yolov2_voc_pruned_0_71 448x448 9.9 350 431.613
104 yolov2_voc_pruned_0_77 448x448 7.8 350 437.692
105 yolov3_adas_pruned_0_9 256x512 5.5 350 723.208
106 yolov3_bdd 288x512 53.7 350 228.916
107 yolov3_voc 416x416 65.4 350 279.206
108 yolov3_voc_tf 416x416 65.6 350 279.332
109 yolov4_leaky_spp_m 416x416 60.1 350 242.952
110 yolov4_leaky_spp_m_pruned_0_36 416x416 38.2 350 234.6
111 ultrafast_pt 288x800 8.4 350 592.002
112 ocr_pt 960x960 875.7 350 24.2049
113 HardNet_MSeg_pt 352x352 22.78 350 158.085
114 drunet_pt 528x608 2.59 350 94.1445
115 person-orientation_pruned_558m_pt 224x112 0.558 350 5444.55
116 ofa_resnet50_0_9B_pt 160x160 0.9 350 2264.41
117 SESR_S_pt 360x640 7.48 350 113.793
118 c2d2_lite 512x512 6.86 350 22.4765
119 ofa_depthwise_res50_pt 176x176 1.25 350 2948.67
120 FairMot_pt 640x480 36 350 277.874
121 mobilenet_v3_small_1_0_tf2 224x224 0.132 350 1963.18
122 clocs 12000x100x4 41 350 11.7131
123 tsd_yolox_pt 640x640 73 350 192.576
124 fadnet_pruned 576x960 154 350 10.0451
125 ssr_pt 256x256 39.72 350 70.7821
126 fadnet 576x960 441 350 9.60453
127 solo_pt 640x640 107 350 24.7739
128 chen_color_resnet18_pt 224x224 3.627 350 2839.65
129 face_mask_detection_pt 512x512 0.593 350 770.836
130 ofa_rcan_latency_pt 360x640 45.7 350 33.3203
131 textmountain_pt 960x960 575.2 350 30.8099
132 vehicle_make_resnet18_pt 224x224 3.627 350 2830.61
133 vehicle_type_resnet18_pt 224x224 3.627 350 2840.23
134 ofa_yolo_pt 640x640 48.88 350 220.236
135 ofa_yolo_pruned_0_30_pt 640x640 34.71 350 276.05
136 ofa_yolo_pruned_0_50_pt 640x640 24.62 350 318.732
137 yolov3-coco_tf2 416x416 65.9 350 276.699
138 movenet_ntd_pt 192x192 0.5 350 2750.94
139 yolov4_416_tf 416x416 60.3 350 240.098
140 yolov4_512_tf 512x512 91.2 350 137.829

VCK5000 Performance with 6PE 350 MHz DPUCVDX8H-aieDWC

The following table lists the throughput performance (in frames/sec or fps) for various neural network samples on the Versal ACAP VCK5000 Gen3x16 with DPUCVDX8H-aieDWC running at 6PE@350 MHz.

Table 2. VCK5000 Performance with 6PE 350 MHz DPUCVDX8H-aieDWC
No Neural Network Input Size GOPS DPU Frequency (MHz) Performance (fps) (Multiple thread)
1 bcc_pt 800x1000 268.9 350 79.0952
2 c2d2_lite 512x512 6.86 350 36.7298
3 densebox_320_320 320x320 0.49 350 4588.44
4 densebox_640_360 360x640 1.1 350 2346.24
5 efficientNet-edgetpu-M_tf 240x240 7.34 350 1377.58
6 efficientNet-edgetpu-S_tf 224x224 4.72 350 2488.34
7 ENet_cityscapes_pt 512x1024 8.6 350 141.03
8 face_landmark 96x72 0.14 350 22440.6
9 face-quality 80x60 0.06 350 31413
10 face-quality_pt 80x60 0.06 350 30992.5
11 facerec_resnet20 112x96 3.5 350 4409.6
12 facerec_resnet64 112x96 11 350 2261.3
13 facerec-resnet20_mixed_pt 112x96 3.5 350 4411.13
14 facereid-large_pt 96x96 0.5 350 20918.3
15 facereid-small_pt 80x80 0.09 350 32349.6
16 fpn 256x512 8.9 350 949.618
17 FPN_Res18_Medical_segmentation 320x320 45.3 350 426.442
18 FPN-resnet18_covid19-seg_pt 352x352 22.7 350 958.147
19 FPN-resnet18_Endov 240x320 13.75 350 399.36
20 hourglass-pe_mpii 256x256 10.2 350 727.146
21 inception_resnet_v2_tf 299x299 26.4 350 491.966
22 inception_v1 224x224 3.2 350 3264.81
23 inception_v1_tf 224x224 3 350 3500.3
24 inception_v2 224x224 4 350 2598.33
25 inception_v2_tf 224x224 3.88 350 330.521
26 inception_v3 299x299 11.4 350 911.529
27 inception_v3_pt 299x299 5.7 350 909.003
28 inception_v3_tf 299x299 11.5 350 914.349
29 inception_v3_tf2 299x299 11.5 350 963.511
30 inception_v4 299x299 24.5 350 504.202
31 inception_v4_2016_09_09_tf 299x299 24.6 350 503.536
32 medical_seg_cell_tf2 128x128 5.3 350 1352.53
33 MLPerf_resnet50_v1.5_tf 224x224 8.19 350 3403.95
34 mlperf_ssd_resnet34_tf 1200x1200 433 350 73.2746
35 mobilenet_1_0_224_tf2 224x224 1.1 350 7820.25
36 mobilenet_edge_0_75_tf 224x224 0.62 350 5689.7
37 mobilenet_edge_1_0_tf 224x224 0.99 350 5176.39
38 mobilenet_v1_0_25_128_tf 128x128 0.027 350 20510
39 mobilenet_v1_0_5_160_tf 160x160 0.15 350 14825.3
40 mobilenet_v1_1_0_224_tf 224x224 1.1 350 8058.3
41 mobilenet_v2 224x224 0.6 350 6928.44
42 mobilenet_v2_1_0_224_tf 224x224 0.6 350 6504.78
43 mobilenet_v2_1_4_224_tf 224x224 1.2 350 4971.92
44 mobilenet_v2_cityscapes_tf 1024x2048 132.7 350 18.4175
45 MT-resnet18_mixed_pt 512x320 13.65 350 421.09
46 multi_task 288x512 14.8 350 659.953
47 multi_task_v3_pt 320x512 25.44 350 203.766
48 ofa_depthwise_res50_pt 176x176 1.25 350 3237.38
49 ofa_yolo_pruned_0_30_pt 640x640 34.71 350 376.219
50 ofa_yolo_pruned_0_50_pt 640x640 24.62 350 441.708
51 openpose_pruned_0_3 368x368 49.9 350 132.641
52 personreid-res18_pt 176x80 1.1 350 8047.31
53 personreid-res50_pt 256x128 5.4 350 3750.97
54 plate_detection 320x320 0.49 350 6612.51
55 plate_num 96x288 1.75 350 2669.07
56 pmg_pt 224x224 2.28 350 3423.98
57

pointpainting_nuscenes_pt

 40000x64x16 112 350 18.0945
58

pointpillars_nuscenes_pt

 40000x64x5 108 350 37.102
59 rcan_pruned_tf 360x640 86.95 350 53.4131
60 refinedet_baseline 480x360 123 350 233.959
61 refinedet_pruned_0_8 360x480 25 350 513.399
62 refinedet_pruned_0_92 360x480 10.1 350 648.891
63 refinedet_pruned_0_96 360x480 5.1 350 686.944
64 refinedet_VOC_tf 320x320 81.9 350 307.637
65 RefineDet-Medical_EDD_tf 320x320 9.8 350 998.943
66 reid 80x160 0.95 350 8310.57
67 resnet_v1_101_tf 224x224 14.4 350 2244.46
68 resnet_v1_152_tf 224x224 21.8 350 1598.02
69 resnet_v1_50_tf 224x224 7 350 3736.48
70 resnet18 224x224 3.7 350 5134.89
71 resnet50 224x224 7.7 350 3738.38
72 resnet50_pt 224x224 4.1 350 3432.18
73 resnet50_tf2 224x224 7.7 350 3152.91
74 retinaface 360x640 1.11 350 1929.07
75 salsanext_pt 64x2048 20.4 350 158.088
76 salsanext_v2_pt 64x2048 32 350 91.2235
77 semantic_seg_citys_tf2 512x1024 54 350 114.931
78 SemanticFPN_cityscapes_pt 256x512 10 350 1049.65
79 SemanticFPN_Mobilenetv2_pt 512x1024 5.4 350 234.421
80 SESR_S_pt 360x640 7.48 350 185.874
81 sp_net 128x224 0.55 350 6726.33
82 squeezenet 227x227 0.76 350 7476.08
83 squeezenet_pt 224x224 0.82 350 7062.16
84 ssd_adas_pruned_0_95 360x480 6.3 350 706.973
85 ssd_inception_v2_coco_tf 300x300 9.6 350 166.214
86 ssd_mobilenet_v1_coco_tf 300x300 2.5 350 2471.66
87 ssd_mobilenet_v2 360x480 6.6 350 803.248
88 ssd_mobilenet_v2_coco_tf 300x300 3.8 350 1879.99
89 ssd_pedestrian_pruned_0_97 360x640 5.9 350 606.687
90 ssd_resnet_50_fpn_coco_tf 640x640 178.4 350 108.15
91 ssd_traffic_pruned_0_9 360x480 11.6 350 691.374
92 ssdlite_mobilenet_v2_coco_tf 300x300 1.5 350 2552.13
93 tiny_yolov3_vmss 416x416 5.46 350 1968.06
94 unet_chaos-CT_pt 512x512 23.3 350 201.477
95 vgg_16_tf 224x224 31 350 500.503
96 vgg_19_tf 224x224 39.3 350 446.076
97 vpgnet_pruned_0_99 480x640 2.5 350 443.372
98 yolov2_voc 448x448 34 350 821.262
99 yolov2_voc_pruned_0_66 448x448 11.6 350 1205.87
100 yolov2_voc_pruned_0_71 448x448 9.9 350 1343.89
101 yolov2_voc_pruned_0_77 448x448 7.8 350 1323.1
102 yolov3_adas_pruned_0_9 256x512 5.5 350 1118.53
103 yolov3_bdd 288x512 53.7 350 319.134
104 yolov3_voc 416x416 65.4 350 389.429
105 yolov3_voc_tf 416x416 65.6 350 389.708
106 yolov4_leaky_spp_m 416x416 60.1 350 324.065
107 yolov4_leaky_spp_m_pruned_0_36 416x416 38.2 350 312.385
108 ultrafast_pt 288x800 8.4 350 1039.88
109 ocr_pt 960x960 875.7 350 11.7934
110 drunet_pt 528x608 2.59 350 153.309
111 person-orientation_pruned_558m_pt 224x112 0.558 350 9448
112 ofa_resnet50_0_9B_pt 160x160 0.9 350 4013.12
113 FairMot_pt 640x480 36 350 426.194
114 tsd_yolox_pt 640x640 73 350 261.897
115 fadnet_pruned 576x960 154 350 10.346
116 ssr_pt 256x256 39.72 350 90.0609
117 fadnet 576x960 441 350 9.23075
118 chen_color_resnet18_pt 224x224 3.627 350 5301.04
119 face_mask_detection_pt 512x512 0.593 350 1335.01
120 ofa_rcan_latency_pt 360x640 45.7 350 50.141
121 textmountain_pt 960x960 575.2 350 41.828
122 vehicle_make_resnet18_pt 224x224 3.627 350 5284.14
123 vehicle_type_resnet18_pt 224x224 3.627 350 5300.12
124 ofa_yolo_pt 640x640 48.88 350 285.311
125 movenet_ntd_pt 192x192 0.5 350 2267.16
126 yolov3-coco_tf2 416x416 65.9 350 385.253

VCK5000 Performance with 6PE 350 MHz DPUCVDX8H-aieMISC

The following table lists the throughput performance (in frames/sec or fps) for various neural network samples on the Versal ACAP VCK5000 Gen3x16 with DPUCVDX8H-aieMISC running at 6PE@350 MHz.

Table 3. VCK5000 Performance with 6PE 350 MHz DPUCVDX8H-aieMISC
No Neural Network Input Size GOPS DPU Frequency (MHz) Performance (fps) (Multiple thread)
1

centerpoint_0_pt

centerpoint_1_pt		 2560x40x4 54 350 10.832
2 densebox_320_320 320x320 0.49 350 4595.14
3 densebox_640_360 360x640 1.1 350 2343.7
4 ENet_cityscapes_pt 512x1024 8.6 350 91.5134
5 face_landmark 96x72 0.14 350 12890.3
6 face-quality 80x60 0.06 350 24403.4
7 face-quality_pt 80x60 0.06 350 24426.3
8 facerec_resnet20 112x96 3.5 350 4398.58
9 facerec_resnet64 112x96 11 350 2250.76
10 facerec-resnet20_mixed_pt 112x96 3.5 350 4397.97
11 facereid-large_pt 96x96 0.5 350 17183.6
12 facereid-small_pt 80x80 0.09 350 25900.5
13 fpn 256x512 8.9 350 558.541
14 FPN_Res18_Medical_segmentation 320x320 45.3 350 225.73
15 FPN-resnet18_covid19-seg_pt 352x352 22.7 350 880.958
16 inception_resnet_v2_tf 299x299 26.4 350 448.058
17 inception_v1 224x224 3.2 350 2122.92
18 inception_v1_tf 224x224 3 350 2224.44
19 inception_v2 224x224 4 350 1761.02
20 inception_v3 299x299 11.4 350 713.317
21 inception_v3_pt 299x299 5.7 350 710.52
22 inception_v3_tf 299x299 11.5 350 711.533
23 inception_v3_tf2 299x299 11.5 350 719.181
24 inception_v4 299x299 24.5 350 398.407
25 inception_v4_2016_09_09_tf 299x299 24.6 350 397.711
26 medical_seg_cell_tf2 128x128 5.3 350 1294.82
27 MLPerf_resnet50_v1.5_tf 224x224 8.19 350 2454.78
28 mlperf_ssd_resnet34_tf 1200x1200 433 350 67.6523
29 multi_task 288x512 14.8 350 629.198
30 openpose_pruned_0_3 368x368 49.9 350 128.641
31 personreid-res18_pt 176x80 1.1 350 7050.56
32 personreid-res50_pt 256x128 5.4 350 3012.43
33 plate_detection 320x320 0.49 350 6612.83
34 plate_num 96x288 1.75 350 2018.92
35 pmg_pt 224x224 2.28 350 2887.85
36

pointpainting_nuscenes_pt

 40000x64x16 112 350 15.5491
37

pointpillars_kitti_pt

 12000x100x4 10.8 350 3.09882
38

pointpillars_nuscenes_pt

 40000x64x5 108 350 34.4647
39 rcan_pruned_tf 360x640 87 350 52.8033
40 refinedet_baseline 480x360 123 350 220.563
41 refinedet_pruned_0_8 360x480 25 350 438.858
42 refinedet_pruned_0_92 360x480 10.1 350 532.618
43 refinedet_pruned_0_96 360x480 5.1 350 559.013
44 refinedet_VOC_tf 320x320 81.9 350 292.194
45 RefineDet-Medical_EDD_tf 320x320 9.8 350 828.599
46 reid 80x160 0.95 350 7429.52
47 resnet_v1_101_tf 224x224 14.4 350 1715.8
48 resnet_v1_152_tf 224x224 21.8 350 1237.54
49 resnet_v1_50_tf 224x224 7 350 2718.97
50 resnet18 224x224 3.7 350 4013.81
51 resnet50 224x224 7.7 350 2722.39
52 resnet50_pt 224x224 4.1 350 2507.43
53 resnet50_tf2 224x224 7.7 350 2306.45
54 salsanext_pt 64x2048 20.4 350 152.583
55 salsanext_v2_pt 64x2048 32 350 83.7139
56 semantic_seg_citys_tf2 512x1024 54 350 80.988
57 SemanticFPN_cityscapes_pt 256x512 10 350 992.311
58 sp_net 128x224 0.55 350 4502.57
59 squeezenet 227x227 0.76 350 4752.42
60 squeezenet_pt 224x224 0.82 350 4617.91
61 ssd_adas_pruned_0_95 360x480 6.3 350 572.152
62 ssd_pedestrian_pruned_0_97 360x640 5.9 350 468.949
63 ssd_resnet_50_fpn_coco_tf 640x640 178 350 105.051
64 ssd_traffic_pruned_0_9 360x480 11.6 350 564.056
65 tiny_yolov3_vmss 416x416 5.46 350 835.482
66 unet_chaos-CT_pt 512x512 23.3 350 185.083
67 vgg_16_tf 224x224 31 350 478.678
68 vgg_19_tf 224x224 39.3 350 428.678
69 vpgnet_pruned_0_99 480x640 2.5 350 347.45
70 yolov2_voc 448x448 34 350 491.411
71 yolov2_voc_pruned_0_66 448x448 11.6 350 608.783
72 yolov2_voc_pruned_0_71 448x448 9.9 350 641.532
73 yolov2_voc_pruned_0_77 448x448 7.8 350 635.995
74 yolov3_adas_pruned_0_9 256x512 5.5 350 1048.86
75 yolov3_bdd 288x512 53.7 350 316.456
76 yolov3_voc 416x416 65.4 350 386.959
77 yolov3_voc_tf 416x416 65.6 350 386.939
78 yolov4_leaky_spp_m 416x416 60.1 350 318.688
79 yolov4_leaky_spp_m_pruned_0_36 416x416 38.2 350 308.202
80 ultrafast_pt 288x800 8.4 350 870.674
81 ocr_pt 960x960 876 350 33.8452
82 drunet_pt 528x608 2.59 350 141.072
83 person-orientation_pruned_558m_pt 224x112 0.56 350 8060.23
84 ofa_resnet50_0_9B_pt 160x160 0.9 350 3305.77
85 SESR_S_pt 360x640 7.48 350 170.614
86 FairMot_pt 640x480 36 350 381.481
87 clocs 12000x100x4 41 350 17.0951
88 tsd_yolox_pt 640x640 73 350 257.268
89 fadnet 576x960 441 350 8.90566
90 solo_pt 640x640 107 350 24.1319
91 chen_color_resnet18_pt 224x224 3.63 350 4166.42
92 ofa_rcan_latency_pt 360x640 45.7 350 49.919
93 textmountain_pt 960x960 575 350 40.6086
94 vehicle_make_resnet18_pt 224x224 3.63 350 4161
95 vehicle_type_resnet18_pt 224x224 3.63 350 4168.73
96 ofa_yolo_pt 640x640 48.9 350 279.768
97 ofa_yolo_pruned_0_30_pt 640x640 34.7 350 366.232
98 ofa_yolo_pruned_0_50_pt 640x640 24.6 350 424.637
99 yolov3-coco_tf2 416x416 65.9 350 382.299
100 yolov4_416_tf 416x416 60.3 350 315.308
101 yolov4_512_tf 512x512 91.2 350 171.517

VCK5000 Performance with 8PE 350 MHz DPUCVDX8H

The following table lists the throughput performance (in frames/sec or fps) for various neural network samples on the Versal ACAP VCK5000 Gen3x16 with DPUCVDX8H running at 8PE@350 MHz.

Table 4. VCK5000 Performance with 8PE350 MHz DPUCVDX8H
No Neural Network Input Size GOPS DPU Frequency (MHz) Performance (fps) (Multiple thread)
1 densebox_320_320 320x320 0.49 350 5973.3
2 densebox_640_360 360x640 1.1 350 3056.58
3 ENet_cityscapes_pt 512x1024 8.6 350 148.317
4 face_landmark 96x72 0.14 350 20304.4
5 face-quality 80x60 0.06 350 31247.3
6 face-quality_pt 80x60 0.06 350 31080.9
7 fpn 256x512 8.9 350 1064.08
8 FPN_Res18_Medical_segmentation 320x320 45.3 350 557.245
9 FPN-resnet18_covid19-seg_pt 352x352 22.7 350 1177
10 inception_v1 224x224 3.2 350 3980.48
11 inception_v1_tf 224x224 3 350 4224.61
12 medical_seg_cell_tf2 128x128 5.3 350 1504.8
13 MLPerf_resnet50_v1.5_tf 224x224 8.19 350 4519.23
14 mlperf_ssd_resnet34_tf 1200x1200 433 350 76.7817
15 multi_task 288x512 14.8 350 721.458
16 openpose_pruned_0_3 368x368 49.9 350 170.355
17 plate_detection 320x320 0.49 350 8171.92
18 plate_num 96x288 1.75 350 3189.72
19 rcan_pruned_tf 360x640 86.95 350 71.193
20 refinedet_baseline 480x360 123 350 285.822
21 refinedet_pruned_0_8 360x480 25 350 669.851
22 refinedet_pruned_0_92 360x480 10.1 350 858.237
23 refinedet_pruned_0_96 360x480 5.1 350 879.11
24 refinedet_VOC_tf 320x320 81.9 350 399.879
25 RefineDet-Medical_EDD_tf 320x320 9.8 350 1265.4
26 reid 80x160 0.95 350 10933.3
27 resnet_v1_101_tf 224x224 14.4 350 2979.15
28 resnet_v1_152_tf 224x224 21.8 350 2122.98
29 resnet_v1_50_tf 224x224 7 350 4944.3
30 resnet18 224x224 3.7 350 6707.05
31 resnet50 224x224 7.7 350 4946.5
32 resnet50_pt 224x224 4.1 350 4544.06
33 resnet50_tf2 224x224 7.7 350 4167.8
34 salsanext_pt 64x2048 20.4 350 159.507
35 salsanext_v2_pt 64x2048 32 350 89.4142
36 semantic_seg_citys_tf2 512x1024 54 350 117.975
37 SemanticFPN_cityscapes_pt 256x512 10 350 1084.85
38 sp_net 128x224 0.55 350 8459.27
39 squeezenet 227x227 0.76 350 8545.25
40 squeezenet_pt 224x224 0.82 350 7536.39
41 ssd_adas_pruned_0_95 360x480 6.3 350 915.896
42 ssd_pedestrian_pruned_0_97 360x640 5.9 350 752.78
43 ssd_resnet_50_fpn_coco_tf 640x640 178.4 350 114.534
44 ssd_traffic_pruned_0_9 360x480 11.6 350 815.91
45 tiny_yolov3_vmss 416x416 5.46 350 2601.98
46 unet_chaos-CT_pt 512x512 23.3 350 247.929
47 vpgnet_pruned_0_99 480x640 2.5 350 619.718
48 yolov2_voc 448x448 34 350 969.973
49 yolov2_voc_pruned_0_66 448x448 11.6 350 1539.63
50 yolov2_voc_pruned_0_71 448x448 9.9 350 1766.91
51 yolov2_voc_pruned_0_77 448x448 7.8 350 1541.77
52 yolov3_adas_pruned_0_9 256x512 5.5 350 1450.82
53 yolov3_bdd 288x512 53.7 350 400.306
54 yolov3_voc 416x416 65.4 350 476.152
55 yolov3_voc_tf 416x416 65.6 350 476.97
56 ultrafast_pt 288x800 8.4 350 1359.92
57 ocr_pt 960x960 875.7 350 12.6949
58 drunet_pt 528x608 2.59 350 204.593
59 SESR_S_pt 360x640 7.48 350 233.238
60 FairMot_pt 640x480 36 350 506.422
61 fadnet 576x960 441 350 9.64873
62 chen_color_resnet18_pt 224x224 3.627 350 6526.42
63 ofa_rcan_latency_pt 360x640 45.7 350 66.8058
64 textmountain_pt 960x960 575.2 350 49.4783
65 vehicle_make_resnet18_pt 224x224 3.627 350 6910.37
66 vehicle_type_resnet18_pt 224x224 3.627 350 6945.23
67 yolov3-coco_tf2 416x416 65.9 350 469.791