U280 Performance - 1.2 English

Vitis AI Library User Guide (UG1354)
Document ID
UG1354
Release Date
2020-07-21
Version
1.2 English

The Xilinx® Alveo U280 Data Center accelerator cards are peripheral component interconnect express ( PCIe® ) Gen3x16 compliant and Gen4x8 compatible cards featuring the Xilinx 16 nm UltraScale+ technology. In this release, DPU is implemented in program logic 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 U280 Gen3x16 with DPU running at 14E@300 MHz.
Note: Some models cannot run at the highest frequency of DPU and need DPU frequency reduction. See the Setting Up the Host for DPU frequency reduction operation.
Table 1. U280 Performance with 14E300Mhz DPU
No Neural Network Input Size GOPS DPU Frequency (Mhz) Performance (fps) (Multiple thread)
1 inception_resnet_v2_tf 299x299 26.4 300x0.5 150.1
2 inception_v1_tf 224x224 3.0 300x0.5 1117.9
3 inception_v3_tf 299x299 11.5 300x0.5 371.8
4 inception_v4_2016_09_09_tf 299x299 24.6 300x0.5 168
5 mobilenet_v1_0_25_128_tf 128x128 0.027 N/A N/A
6 mobilenet_v1_0_5_160_tf 160x160 0.15 N/A N/A
7 mobilenet_v1_1_0_224_tf 224x224 1.1 N/A N/A
8 mobilenet_v2_1_0_224_tf 224x224 0.60 N/A N/A
9 mobilenet_v2_1_4_224_tf 224x224 1.2 N/A N/A
10 resnet_v1_101_tf 224x224 14.4 300x0.5 387.5
11 resnet_v1_152_tf 224x224 21.8 300x0.5 258.9
12 resnet_v1_50_tf 224x224 7.0 300x0.6 890.3
13 vgg_16_tf 224x224 31.0 300x0.5 182.7
14 vgg_19_tf 224x224 39.3 300x0.5 153.1
15 ssd_mobilenet_v1_coco_tf 300x300 2.5 N/A N/A
16 ssd_mobilenet_v2_coco_tf 300x300 3.8 N/A N/A
17 ssd_resnet_50_fpn_coco_tf 640x640 178.4 300x0.5 28.8
18 yolov3_voc_tf 416x416 65.6 300x0.6 112.4
19 mlperf_ssd_resnet34_tf 1200x1200 433 N/A N/A
20 resnet50 224x224 7.7 300x0.7 918.1
21 resnet18 224x224 3.7 300x0.5 1634.4
22 inception_v1 224x224 3.2 300x0.5 1069.5
23 inception_v2 224x224 4.0 300x0.5 937
24 inception_v3 299x299 11.4 300x0.5 372
25 inception_v4 299x299 24.5 300x0.5 167
26 mobilenet_v2 224x224 0.6 N/A N/A
27 squeezenet 227x227 0.76 300x0.5 2821.7
28 ssd_pedestrain_pruned_0_97 360x360 5.9 300x0.5 423.3
29 ssd_traffic_pruned_0_9 360x480 11.6 300x0.5 306
30 ssd_adas_pruned_0_95 360x480 6.3 300x0.5 476.1
31 ssd_mobilenet_v2 360x480 6.6 N/A N/A
32 refinedet_pruned_0_8 360x480 25 N/A N/A
33 refinedet_pruned_0_92 360x480 10.1 N/A N/A
34 refinedet_pruned_0_96 360x480 5.1 N/A N/A
35 vpgnet_pruned_0_99 480x640 2.5 300x0.5 567.8
36 fpn 256x512 8.9 300x0.5 362.9
37 sp_net 128x224 0.55 300x0.5 2126.6
38 openpose_pruned_0_3 368x368 49.9 300x0.5 36.5
39 densebox_320_320 320x320 0.49 300x0.5 2622.3
40 densebox_640_360 360x640 1.1 300x0.5 1138.8
41 face_landmark 96x72 0.14 300x0.5 11302.4
42 reid 80x160 0.95 300x0.5 4608
43 multi_task 288x512 14.8 300x0.5 128.3
44 yolov3_adas_pruned_0_9 256x512 5.5 300x0.6 893.1
45 yolov3_voc 416x416 65.4 300x0.6 113.6
46 yolov3_bdd 288x512 53.7 300x0.6 108.6
47 yolov2_voc 448x448 34 N/A N/A
48 yolov2_voc_pruned_0_66 448x448 11.6 300x0.5 490.3
49 yolov2_voc_pruned_0_71 448x448 9.9 300x0.5 570.3
50 yolov2_voc_pruned_0_77 448x448 7.8 300x0.5 679.6
51 facerec_resnet20 112x96 3.5 300x0.5 1576.9
52 facerec_resnet64 112x96 11.0 300x0.5 575.4
53 plate_detection 320x320 0.49 300x0.5 4235.7
54 plate_recognition 96x288 1.75 N/A N/A
55 FPN_Res18_Medical_segmentation 320x320 45.3 300x0.5 104.9
56 refinedet_baseline 480x360 123 N/A N/A
57 resnet50_pt 224x224 4.1 300x0.7 878.4
58 squeezenet_pt 224x224 0.82 300x0.5 1655.7
59 inception_v3_pt 299x299 5.7 300x0.5 371