ZCU102 Evaluation Kit - ZCU102 Evaluation Kit - 2.5 English - UG1354

Vitis AI Library User Guide (UG1354)
Document ID
UG1354
Release Date
2022-06-15
Version
2.5 English

The ZCU102 evaluation kit uses the mid-range ZU9 UltraScale+™ device. There are two different hardware versions of the ZCU102 evaluation kit, one with the serial number 0432055-04 as the header, and the other with the serial number 0432055-05 as the header. The performance of the Vitis AI Library varies between the two hardware versions (because of different DDR memory performance). Because the 0432055-04 version of ZCU102 has been discontinued, the following table only shows the performance for the ZCU102 (0432055-05) evaluation kit. In the ZCU102 evaluation kit, three B4096 DPU cores are implemented in the program logic and delivers 3.45 TOPS INT8 peak performance for deep learning inference acceleration.

Refer to the following table for throughput performance (in frames/sec or fps) for various neural network samples on ZCU102 (0432055-05) with DPU running at 281 MHz.

Note: The DPU on the ZCU102 has a hardware softmax acceleration module. Due to the limitation of the hardware softmax module, the software softmax is faster when the number of categories reaches 1000. Set XLNX_ENABLE_C_SOFTMAX=1 to enable the software softmax: softmax_c. The default value of XLNX_ENABLE_C_SOFTMAX is 0, which means the softmax method is selected according to the following priorities.
  1. Neon Acceleration
  2. Hardware Softmax
  3. Software Softmax_c

For ZCU102, use the following command to test the performance of classification.

env XLNX_ENABLE_C_SOFTMAX=1 ./test_performance_classification resnet50 test_performance_classification.list -t 8 -s 60
Table 1. ZCU102 (0432055-05) Performance
No Neural Network Input Size GOPS Performance (fps) (Single thread) Performance (fps) (Multiple thread)
1 bcc_pt 800x1000 268.9 3.3 10.9
2 c2d2_lite 512x512 6.86 2.8 5.2
3 centerpoint 2560x40x4 54 16 47.9
4 chen_color_resnet18_pt 224x224 3.627 204.7 499.9
5 clocs 12000x100x4 41 2.8 10.1
6 densebox_320_320 320x320 0.49 501.6 1750.8
7 densebox_640_360 360x640 1.1 249.4 864.5
8 drunet_pt 528x608 2.59 60.8 189.3
9 efficientdet_d2_tf 768x768 11.06 3.1 6
10 efficientnet-b0_tf2 224x224 0.36 78 152.6
11 efficientNet-edgetpu-L_tf 300x300 19.36 35 90.2
12 efficientNet-edgetpu-M_tf 240x240 7.34 79.9 205.2
13 efficientNet-edgetpu-S_tf 224x224 4.72 115 308.1
14 ENet_cityscapes_pt 512x1024 8.6 10.1 36.3
15 face_landmark 96x72 0.14 959.8 1601.8
16 face_mask_detection_pt 512x512 0.593 115.7 400.8
17 face-quality 80x60 0.06 3038.2 8824.5
18 face-quality_pt 80x60 0.06 2981.6 8775.5
19 facerec_resnet20 112x96 3.5 168.4 338
20 facerec_resnet64 112x96 11 73.3 183.4
21 facerec-resnet20_mixed_pt 112x96 3.5 168.8 338.3
22 facereid-large_pt 96x96 0.5 941.5 2276.7
23 facereid-small_pt 80x80 0.09 2243.1 6336.8
24 fadnet 576x960 441 1.2 1.6
25 fadnet_pruned 576x960 154 1.8 2.7
26 FairMot_pt 640x480 36 22.6 66
27 fpn 256x512 8.9 34.7 149.8
28 FPN_Res18_Medical_segmentation 320x320 45.3 12.6 47.3
29 FPN-resnet18_covid19-seg_pt 352x352 22.7 37.1 107.2
30 FPN-resnet18_Endov 240x320 13.75 37.4 155.8
31 HardNet_MSeg_pt 352x352 22.78 24.7 56.8
32 hfnet_tf 960x960 20.09 3.6 15.9
33 hourglass-pe_mpii 256x256 10.2 18.8 70.6
34 inception_resnet_v2_tf 299x299 26.4 23.9 52
35 inception_v1 224x224 3.2 187.6 471.6
36 inception_v1_tf 224x224 3 191.3 470.6
37 inception_v2 224x224 4 136.3 303.9
38 inception_v2_tf 224x224 3.88 93 229.8
39 inception_v3 299x299 11.4 59.9 135.8
40 inception_v3_pt 299x299 5.7 59.8 136
41 inception_v3_tf 299x299 11.5 59.8 135.3
42 inception_v3_tf2 299x299 11.5 59.2 136.4
43 inception_v4 299x299 24.5 28.8 68.5
44 inception_v4_2016_09_09_tf 299x299 24.6 28.8 68.6
45 medical_seg_cell_tf2 128x128 5.3 154.3 393.1
46 MLPerf_resnet50_v1.5_tf 224x224 8.19 78.9 173.6
47 mlperf_ssd_resnet34_tf 1200x1200 433 1.9 7.1
48 mobilenet_1_0_224_tf2 224x224 1.1 321.5 939.6
49 mobilenet_edge_0_75_tf 224x224 0.62 262.6 720.5
50 mobilenet_edge_1_0_tf 224x224 0.99 214.7 554.8
51 mobilenet_v1_0_25_128_tf 128x128 0.027 1331.1 4759.2
52 mobilenet_v1_0_5_160_tf 160x160 0.15 914.6 3130.3
53 mobilenet_v1_1_0_224_tf 224x224 1.1 326.8 951.7
54 mobilenet_v2 224x224 0.6 274.9 745.5
55 mobilenet_v2_1_0_224_tf 224x224 0.6 267.9 707.5
56 mobilenet_v2_1_4_224_tf 224x224 1.2 191.1 473
57 mobilenet_v2_cityscapes_tf 1024x2048 132.74 1.7 5.3
58 mobilenet_v3_small_1_0_tf2 224x224 0.132 338.1 966.3
59 movenet_ntd_pt 192x192 0.5 94.1 390.9
60 MT-resnet18_mixed_pt 512x320 13.65 32.8 103
61 multi_task 288x512 14.8 39.7 129
62 multi_task_v3_pt 320x512 25.44 17.1 61.3
63 ocr_pt 960x960 875.7 1.1 3.4
64 ofa_depthwise_res50_pt 176x176 1.25 106 369.1
65 ofa_rcan_latency_pt 360x640 45.7 17 28.1
66 ofa_resnet50_0_9B_pt 160x160 0.9 183.9 354.8
67 ofa_yolo_pruned_0_30_pt 640x640 34.71 21.5 54.5
68 ofa_yolo_pruned_0_50_pt 640x640 24.62 27.7 71.3
69 ofa_yolo_pt 640x640 48.88 16.9 42.8
70 openpose_pruned_0_3 368x368 49.9 3.8 15.1
71 person-orientation_pruned_558m_pt 224x112 0.558 661.1 1428.5
72 personreid-res18_pt 176x80 1.1 370.6 690.7
73 personreid-res50_pt 256x128 5.4 107.5 237.7
74 plate_detect 320x320 0.49 628.3 2242.2
75 plate_num 96x288 1.75 189.2 548.2
76 pmg_pt 224x224 2.28 151.7 366.9
77 pointpainting_nuscenes_pt 40000x64x16 112 1.3 4.2
78 pointpillars_kitti_pt 12000x100x4 10.8 19.6 49.3
79 pointpillars_nuscenes_pt 40000x64x5 108 2.2 9.5
80 rcan_pruned_tf 360x640 86.95 8.6 18
81 refinedet_baseline 480x360 123 8.6 24.8
82 refinedet_pruned_0_8 360x480 25 33 98.4
83 refinedet_pruned_0_92 360x480 10.1 65.6 199.5
84 refinedet_pruned_0_96 360x480 5.1 92.2 282.9
85 refinedet_VOC_tf 320x320 81.9 11.3 34.5
86 RefineDet-Medical_EDD_tf 320x320 9.8 67.6 229.4
87 reid 80x160 0.95 371 699.3
88 resnet_v1_101_tf 224x224 14.4 46.4 110.7
89 resnet_v1_152_tf 224x224 21.8 31.7 77.3
90 resnet_v1_50_tf 224x224 7 87.9 191.1
91 resnet_v2_101_tf 299x299 26.78 23.6 55.6
92 resnet_v2_152_tf 299x299 40.47 16.1 38.2
93 resnet_v2_50_tf 299x299 13.1 45 99.7
94 resnet18 224x224 3.7 196.9 488.4
95 resnet50 224x224 7.7 88.4 193.8
96 resnet50_pt 224x224 4.1 78.3 173.4
97 resnet50_tf2 224x224 7.7 87.3 192.9
98 retinaface 360x640 1.11 140 567.6
99 SA_gate_base_pt 360x360 178 3.3 9.5
100 salsanext_pt 64x2048 20.4 5.6 21.3
101 salsanext_v2_pt 64x2048 32 4.1 11
102 semantic_seg_citys_tf2 512x1024 54 7.4 23.9
103 SemanticFPN_cityscapes_pt 256x512 10 35.5 161.9
104 SemanticFPN_Mobilenetv2_pt 512x1024 5.4 10.5 52.5
105 SESR_S_pt 360x640 7.48 88.2 140.6
106 solo_pt 640x640 107 1.4 4.8
107 sp_net 128x224 0.55 583.4 1632.8
108 squeezenet 227x227 0.76 543.5 1435.5
109 squeezenet_pt 224x224 0.82 575.4 1498.6
110 ssd_adas_pruned_0_95 360x480 6.3 91.7 296.4
111 ssd_inception_v2_coco_tf 300x300 9.6 39.4 102.2
112 ssd_mobilenet_v1_coco_tf 300x300 2.5 110.9 331.1
113 ssd_mobilenet_v2 360x480 6.6 40.7 117.5
114 ssd_mobilenet_v2_coco_tf 300x300 3.8 81.3 213.3
115 ssd_pedestrian_pruned_0_97 360x360 5.9 80.7 278
116 ssd_resnet_50_fpn_coco_tf 640x640 178.4 2.9 5.2
117 ssd_traffic_pruned_0_9 360x480 11.6 56.4 199.8
118 ssdlite_mobilenet_v2_coco_tf 300x300 1.5 105.6 305.7
119 ssr_pt 256x256 39.72 6 14.4
120 superpoint_tf 480x640 52.4 12.6 53.8
121 textmountain_pt 960x960 575.2 1.7 4.7
122 tiny_yolov3_vmss 416x416 5.46 125.9 393.9
123 tsd_yolox_pt 640x640 73 13.1 33.7
124 ultrafast_pt 288x800 8.4 35.1 95.9
125 unet_chaos-CT_pt 512x512 23.3 22.8 69.7
126 vehicle_make_resnet18_pt 224x224 3.627 203.4 498.6
127 vehicle_type_resnet18_pt 224x224 3.627 205 500.6
128 vgg_16_tf 224x224 31 20.2 40.9
129 vgg_19_tf 224x224 39.3 17.4 36.5
130 vpgnet_pruned_0_99 480x640 2.5 104.8 354.3
131 yolov2_voc 448x448 34 26.6 69.2
132 yolov2_voc_pruned_0_66 448x448 11.6 65.5 191.8
133 yolov2_voc_pruned_0_71 448x448 9.9 75.3 224.1
134 yolov2_voc_pruned_0_77 448x448 7.8 89.2 269.4
135 yolov3_adas_pruned_0_9 256x512 5.5 98.4 271.8
136 yolov3_bdd 288x512 53.7 12.9 33.9
137 yolov3_coco_416_tf2 416x416 65.9 13.2 34.9
138 yolov3_voc 416x416 65.4 13.4 34.9
139 yolov3_voc_tf 416x416 65.6 13.6 35.1
140 yolov4_leaky_416_tf 416x416 60.3 13.5 34
141 yolov4_leaky_512_tf 512x512 91.2 10.2 25.3
142 yolov4_leaky_spp_m 416x416 60.1 13.7 34.3
143 yolov4_leaky_spp_m_pruned_0_36 416x416 38.2 18.9 46.6