VCK5000 Evaluation Kit - 1.4.1 English

Vitis AI Library User Guide (UG1354)
Document ID
UG1354
Release Date
2021-12-11
Version
1.4.1 English

The Versal ACAP VCK5000 is a Versal AI Core series evaluation kit that enables 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, DPU core with batch=8 is implemented using AI Engines.

VCK5000 Performance with 8PE350 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 1. 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 5902.4
2 densebox_640_360 360x640 1.1 350 2802.39
3 ENet_cityscapes_pt 512x1024 8.6 350 140.057
4 face_landmark 96x72 0.14 350 14111.7
5 face-quality_pt 80x60 0.06 350 41833.6
6 fpn 256x512 8.9 350 1074.4
7 FPN_Res18_Medical_segmentation 320x320 45.3 350 535.843
8 FPN-resnet18_covid19-seg_pt 352x352 22.7 350 1071.96
9 inception_v1 224x224 3.2 350 4105.35
10 inception_v1_tf 224x224 3 350 4362.47
11 medical_seg_cell_tf2 128x128 5.3 350 1955.31
12 MLPerf_resnet50_v1.5_tf 224x224 8.19 350 4425.9
13 multi_task 288x512 14.8 350 694.169
14 openpose_pruned_0_3 368x368 49.9 350 168.812
15 plate_detection 320x320 0.49 350 7812.45
16 refinedet_baseline 480x360 123 350 291.851
17 RefineDet-Medical_EDD_tf 320x320 9.8 350 1287.96
18 refinedet_pruned_0_8 360x480 25 350 654.851
19 refinedet_pruned_0_92 360x480 10.1 350 844.655
20 refinedet_pruned_0_96 360x480 5.1 350 888.547
21 refinedet_VOC_tf 320x320 81.9 350 378.612
22 reid 80x160 0.95 350 10467.2
23 resnet18 224x224 3.7 350 6434.83
24 resnet50 224x224 7.7 350 4516.51
25 resnet50_pt 224x224 4.1 350 4453.62
26 resnet50_tf2 224x224 7.7 350 4515.15
27 resnet_v1_101_tf 224x224 14.4 350 2938.99
28 resnet_v1_152_tf 224x224 21.8 350 2095.6
29 resnet_v1_50_tf 224x224 7 350 4846.78
30 salsanext_pt 64x2048 20.4 350 171.977
31 SemanticFPN_cityscapes_pt 256x512 10 350 1080.18
32 sp_net 128x224 0.55 350 7654.76
33 squeezenet 227x227 0.76 350 8256.32
34 squeezenet_pt 224x224 0.82 350 5147.72
35 ssd_adas_pruned_0_95 360x480 6.3 350 930.74
36 ssd_traffic_pruned_0_9 360x480 11.6 350 894.817
37 tiny_yolov3_vmss 416x416 5.46 350 2612.41
38 unet_chaos-CT_pt 512x512 23.3 350 229.231
39 vpgnet_pruned_0_99 480x640 2.5 350 600.461
40 yolov2_voc 448x448 34 350 949.278
41 yolov2_voc_pruned_0_66 448x448 11.6 350 1574.72
42 yolov2_voc_pruned_0_71 448x448 9.9 350 1727.27
43 yolov2_voc_pruned_0_77 448x448 7.8 350 1698.6
44 yolov3_adas_pruned_0_9 256x512 5.5 350 1379.55
45 yolov3_bdd 288x512 53.7 350 383.884
46 yolov3_voc 416x416 65.4 350 459.207
47 yolov3_voc_tf 416x416 65.6 350 459.307