VCK190 Evaluation Board - VCK190 Evaluation Board - 2.5 English - UG1354

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

VCK190 is the first Versal AI Core series evaluation kit, enabling designers to develop solutions using AI and DSP engines capable of delivering over 100X greater compute performance compared to current server class CPUs. For this release, a C32B6 DPU core is implemented using AI Engine and delivers 61.4 TOPS INT8 peak performance for deep learning inference acceleration.

Refer to the following table for the throughput performance (in frames/sec or fps) for various neural network samples on VCK190 with AI Engines running at 1250 MHz and PL running at 333 MHz.

Table 1. VCK190 Performance with Batch 6
No Neural Network Input Size GOPS Performance (fps) (Single thread) Performance (fps) (Multiple thread)
1 bcc_pt 800x1000 268.9 36.9 72.4
2 c2d2_lite 512x512 6.86 20.4 26.3
3 centerpoint 2560x40x4 54 128 237.8
4 chen_color_resnet18_pt 224x224 3.627 2046.3 5223.5
5 clocs 12000x100x4 41 8.1 14.4
6 densebox_320_320 320x320 0.49 1286.9 2570.9
7 densebox_640_360 360x640 1.1 629.6 1204.2
8 drunet_pt 528x608 2.59 256 462.1
9 efficientdet_d2_tf 768x768 11.06 18.1 40.1
10 efficientnet-b0_tf2 224x224 0.36 1088.6 1812.4
11 efficientNet-edgetpu-L_tf 300x300 19.36 397.3 512.1
12 efficientNet-edgetpu-M_tf 240x240 7.34 860.4 1397
13 efficientNet-edgetpu-S_tf 224x224 4.72 1210.3 2196.1
14 ENet_cityscapes_pt 512x1024 8.6 23.6 54.4
15 face_landmark 96x72 0.14 9846.8 24605.3
16 face_mask_detection_pt 512x512 0.593 453.8 923.1
17 face-quality 80x60 0.06 13848.3 29954.4
18 face-quality_pt 80x60 0.06 13791.3 29820.2
19 facerec_resnet20 112x96 3.5 3865.1 5695.3
20 facerec_resnet64 112x96 11 2185.2 2673
21 facerec-resnet20_mixed_pt 112x96 3.5 3855 5693.6
22 facereid-large_pt 96x96 0.5 7564 19131.6
23 facereid-small_pt 80x80 0.09 11372.4 25915.7
24 fadnet 576x960 441 8 13.6
25 fadnet_pruned 576x960 154 8.4 15.9
26 FairMot_pt 640x480 36 194.7 365
27 fpn 256x512 8.9 103.9 221.3
28 FPN_Res18_Medical_segmentation 320x320 45.3 74.3 195.2
29 FPN-resnet18_covid19-seg_pt 352x352 22.7 480.8 784.7
30 FPN-resnet18_Endov 240x320 13.75 81.8 170.1
31 HardNet_MSeg_pt 352x352 22.78 219.4 274
32 hfnet_tf 960x960 20.09 9.3 22.4
33 hourglass-pe_mpii 256x256 10.2 78 133.8
34 inception_resnet_v2_tf 299x299 26.4 387.6 500.9
35 inception_v1 224x224 3.2 1308 2432.5
36 inception_v1_tf 224x224 3 1305.5 2462.2
37 inception_v2 224x224 4 1100.2 1845.8
38 inception_v2_tf 224x224 3.88 829.2 1196.5
39 inception_v3 299x299 11.4 590.9 899
40 inception_v3_pt 299x299 5.7 594.2 897.7
41 inception_v3_tf 299x299 11.5 597 905.1
42 inception_v3_tf2 299x299 11.5 657.4 1062.1
43 inception_v4 299x299 24.5 340.5 420.1
44 inception_v4_2016_09_09_tf 299x299 24.6 342.1 423.9
45 medical_seg_cell_tf2 128x128 5.3 1536 3036.3
46 MLPerf_resnet50_v1.5_tf 224x224 8.19 1367.7 2744.2
47 mlperf_ssd_resnet34_tf 1200x1200 433 11.9 20.9
48 mobilenet_1_0_224_tf2 224x224 1.1 2031.1 5024.9
49 mobilenet_edge_0_75_tf 224x224 0.62 1902.8 4940.2
50 mobilenet_edge_1_0_tf 224x224 0.99 1835.3 4746.8
51 mobilenet_v1_0_25_128_tf 128x128 0.027 5085.4 10391.8
52 mobilenet_v1_0_5_160_tf 160x160 0.15 3512.5 7804.8
53 mobilenet_v1_1_0_224_tf 224x224 1.1 2015.4 4859.5
54 mobilenet_v2 224x224 0.6 1928.2 4954.6
55 mobilenet_v2_1_0_224_tf 224x224 0.6 1899.3 4996.3
56 mobilenet_v2_1_4_224_tf 224x224 1.2 1538.8 4186.2
57 mobilenet_v2_cityscapes_tf 1024x2048 132.74 4.9 11.9
58 mobilenet_v3_small_1_0_tf2 224x224 0.132 2041.2 4910.7
59 movenet_ntd_pt 192x192 0.5 241.8 443
60 MT-resnet18_mixed_pt 512x320 13.65 131.7 257.5
61 multi_task 288x512 14.8 161.9 288
62 multi_task_v3_pt 320x512 25.44 77.8 174.6
63 ocr_pt 960x960 875.7 8.6 18.5
64 ofa_depthwise_res50_pt 176x176 1.25 302.9 450
65 ofa_rcan_latency_pt 360x640 45.7 59 81.5
66 ofa_resnet50_0_9B_pt 160x160 0.9 2090.9 4009.4
67 ofa_yolo_pruned_0_30_pt 640x640 34.71 145.8 255
68 ofa_yolo_pruned_0_50_pt 640x640 24.62 157.4 297
69 ofa_yolo_pt 640x640 48.88 127.7 220.7
70 openpose_pruned_0_3 368x368 49.9 23.5 35.4
71 person-orientation_pruned_558m_pt 224x112 0.558 5545.9 12380.2
72 personreid-res18_pt 176x80 1.1 4237.7 8631.8
73 personreid-res50_pt 256x128 5.4 1824.6 3789.4
74 plate_detect 320x320 0.49 1550.6 2969.4
75 plate_num 96x288 1.75 1158.7 2175.9
76 pmg_pt 224x224 2.28 1747.6 3627.9
77 pointpainting_nuscenes_pt 40000x64x16 112 3.8 6.6
78 pointpillars_kitti_pt 12000x100x4 10.8 25.3 34.8
79 pointpillars_nuscenes_pt 40000x64x5 108 7.7 16
80 psmnet 576x960 696 0.4 0.7
81 rcan_pruned_tf 360x640 86.95 46 56.9
82 refinedet_baseline 480x360 123 178.8 239.2
83 refinedet_pruned_0_8 360x480 25 317.6 617.6
84 refinedet_pruned_0_92 360x480 10.1 431.8 846.4
85 refinedet_pruned_0_96 360x480 5.1 502.2 915.1
86 refinedet_VOC_tf 320x320 81.9 105.1 226.4
87 RefineDet-Medical_EDD_tf 320x320 9.8 550.7 1283.6
88 reid 80x160 0.95 3998.5 8620
89 resnet_v1_101_tf 224x224 14.4 1064 1756.1
90 resnet_v1_152_tf 224x224 21.8 839 1216.8
91 resnet_v1_50_tf 224x224 7 1452.1 3067.5
92 resnet_v2_101_tf 299x299 26.78 412.8 537.9
93 resnet_v2_152_tf 299x299 40.47 330.9 406.6
94 resnet_v2_50_tf 299x299 13.1 548.6 792.9
95 resnet18 224x224 3.7 1808.3 4498.9
96 resnet50 224x224 7.7 1466.8 3079.1
97 resnet50_pt 224x224 4.1 1372.1 2772.1
98 resnet50_tf2 224x224 7.7 1463.5 3111.4
99 retinaface 360x640 1.11 408.1 835.6
100 SA_gate_base_pt 360x360 178 7.6 9.7
101 salsanext_pt 64x2048 20.4 12.3 23.9
102 salsanext_v2_pt 64x2048 32 10.6 22.3
103 semantic_seg_citys_tf2 512x1024 54 20.5 47.8
104 SemanticFPN_cityscapes_pt 256x512 10 110.5 218.2
105 SemanticFPN_Mobilenetv2_pt 512x1024 5.4 27.2 55.9
106 SESR_S_pt 360x640 7.48 339.8 634.9
107 solo_pt 640x640 107 4.3 7.3
108 sp_net 128x224 0.55 2704 5816.5
109 squeezenet 227x227 0.76 3144.1 5625.9
110 squeezenet_pt 224x224 0.82 3165.4 5758.6
111 ssd_adas_pruned_0_95 360x480 6.3 454.6 825.6
112 ssd_inception_v2_coco_tf 300x300 9.6 266.3 415.5
113 ssd_mobilenet_v1_coco_tf 300x300 2.5 433.5 529.5
114 ssd_mobilenet_v2 360x480 6.6 82.5 157.9
115 ssd_mobilenet_v2_coco_tf 300x300 3.8 387.2 506.7
116 ssd_pedestrian_pruned_0_97 360x360 5.9 383.4 645.7
117 ssd_resnet_50_fpn_coco_tf 640x640 178.4 10.9 12.2
118 ssd_traffic_pruned_0_9 360x480 11.6 337.2 616.7
119 ssdlite_mobilenet_v2_coco_tf 300x300 1.5 385.2 517.7
120 ssr_pt 256x256 39.72 74 77.5
121 superpoint_tf 480x640 52.4 49.8 104.3
122 textmountain_pt 960x960 575.2 20.7 29.6
123 tiny_yolov3_vmss 416x416 5.46 719.7 1424.4
124 tsd_yolox_pt 640x640 73 134 191.1
125 ultrafast_pt 288x800 8.4 392.4 966.1
126 unet_chaos-CT_pt 512x512 23.3 74.6 238.6
127 vehicle_make_resnet18_pt 224x224 3.627 1996.6 5111.8
128 vehicle_type_resnet18_pt 224x224 3.627 2050.7 5155.6
129 vgg_16_tf 224x224 31 531.4 657.2
130 vgg_19_tf 224x224 39.3 482.1 584
131 vpgnet_pruned_0_99 480x640 2.5 357.4 722.6
132 yolov2_voc 448x448 34 418.1 803
133 yolov2_voc_pruned_0_66 448x448 11.6 455.1 1284.1
134 yolov2_voc_pruned_0_71 448x448 9.9 575.4 1330.1
135 yolov2_voc_pruned_0_77 448x448 7.8 598.8 1366
136 yolov3_adas_pruned_0_9 256x512 5.5 524.1 1049.7
137 yolov3_bdd 288x512 53.7 205.3 292.9
138 yolov3_coco_416_tf2 416x416 65.9 179.9 290.1
139 yolov3_voc 416x416 65.4 216.6 289.4
140 yolov3_voc_tf 416x416 65.6 217.6 289.5
141 yolov4_leaky_416_tf 416x416 60.3 141.2 214.6
142 yolov4_leaky_512_tf 512x512 91.2 104.7 154.6
143 yolov4_leaky_spp_m 416x416 60.1 146.1 218.3
144 yolov4_leaky_spp_m_pruned_0_36 416x416 38.2 156.6 241.5