Alveo U280 Data Accelerator Card

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.

Note: Some models cannot run at the highest frequency of DPU and need DPU frequency reduction. See the For Edge for DPU frequency reduction operation.

U280 Performance with 14E300 MHz DPUCAHX8H

Refer to the following table for the throughput performance (in frames/sec or fps) for various neural network samples on U280 with DPUCAHX8H running at 14E@300 MHz.

Table 1. U280 Performance with 14E300 MHz DPUCAHX8H
No	Neural Network	Input Size	GOPS	DPU Frequency (MHz)	Performance (fps) (Multiple thread)
1	densebox_320_320	320x320	0.49	300x0.5	4845.4
2	densebox_640_360	360x640	1.1	300x0.5	2135.5
3	ENet_cityscapes_pt	512x1024	8.6	300x0.5	123.0
4	face_landmark	96x72	0.14	300x0.5	12527.7
5	face-quality	80x60	0.06	300x0.5	28736.4
6	face-quality_pt	80x60	0.06	300x0.5	28616.8
7	facerec_resnet20	112x96	3.5	300x0.5	1599.5
8	facerec-resnet20_mixed_pt	112x96	3.5	300x0.5	1598.9
9	facerec_resnet64	112x96	11	300x0.5	579.2
10	facereid-large_pt	96x96	0.5	300x0.5	11573.5
11	facereid-small_pt	80x80	0.09	300x0.5	30606.7
12	fpn	256x512	8.9	300x0.5	478.4
13	FPN_Res18_Medical_segmentation	320x320	45.3	300x0.5	116.6
14	FPN-resnet18_covid19-seg_pt	352x352	22.7	300x0.5	263.5
15	inception_resnet_v2_tf	299x299	26.4	300x0.5	192.5
16	inception_v1	224x224	3.2	300x0.5	1378.1
17	inception_v1_tf	224x224	3	300x0.5	1430.6
18	inception_v2	224x224	4	300x0.5	1100.6
19	inception_v3	299x299	11.4	300x0.5	443.8
20	inception_v3_pt	299x299	5.7	300x0.5	444.2
21	inception_v3_tf	299x299	11.5	300x0.5	444.3
22	inception_v3_tf2	299x299	11.5	300x0.5	454.1
23	inception_v4	299x299	24.5	300x0.5	207.9
24	inception_v4_2016_09_09_tf	299x299	24.6	300x0.5	208.2
25	medical_seg_cell_tf2	128x128	5.3	300x0.5	1287.3
26	MLPerf_resnet50_v1.5_tf	224x224	8.19	300x0.5	645.7
27	mlperf_ssd_resnet34_tf	1200x1200	433	300x0.5	16.4
28	multi_task	288x512	14.8	300x0.5	353.8
29	openpose_pruned_0_3	368x368	49.9	300x0.5	37.4
30	personreid-res18_pt	176x80	1.1	300x0.5	4540.5
31	personreid-res50_pt	256x128	5.4	300x0.5	1042.6
32	plate_detection	320x320	0.49	300x0.5	8251.1
33	plate_num	96x288	1.75	300x0.5	1497.6
34	pmg_pt	224x224	2.28	300x0.5	1270.3
35	refinedet_baseline	480x360	123	300x0.5	60.5
36	RefineDet-Medical_EDD_tf	320x320	9.8	300x0.5	513.7
37	refinedet_pruned_0_8	360x480	25	300x0.5	220.3
38	refinedet_pruned_0_92	360x480	10.1	300x0.5	458.3
39	refinedet_pruned_0_96	360x480	5.1	300x0.5	647.4
40	refinedet_VOC_tf	320x320	81.9	300x0.5	87.9
41	reid	80x160	0.95	300x0.5	4770.5
42	resnet18	224x224	3.7	300x0.5	1658.3
43	resnet50	224x224	7.7	300x0.6	800.1
44	resnet50_pt	224x224	4.1	300x0.6	774.9
45	resnet50_tf2	224x224	7.7	300x0.5	666.2
46	resnet_v1_101_tf	224x224	14.4	300x0.5	389.0
47	resnet_v1_152_tf	224x224	21.8	300x0.5	259.4
48	resnet_v1_50_tf	224x224	7	300x0.5	750.0
49	salsanext_pt	64x2048	20.4	300x0.9	108.5
50	salsanext_v2_pt	64x2048	32	300x0.5	54.2
51	SemanticFPN_cityscapes_pt	256x512	10	300x0.5	540.2
52	semantic_seg_citys_tf2	512x1024	54	300x0.5	67.1
53	sp_net	128x224	0.55	300x0.5	4071.6
54	squeezenet	227x227	0.76	300x0.5	3961.0
55	squeezenet_pt	224x224	0.82	300x0.5	2187.5
56	ssd_adas_pruned_0_95	360x480	6.3	300x0.5	656.4
57	ssd_pedestrian_pruned_0_97	360x360	5.9	300x0.5	575.9
58	ssd_resnet_50_fpn_coco_tf	640x640	178.4	300x0.5	37.2
59	ssd_traffic_pruned_0_9	360x480	11.6	300x0.5	435.2
60	tiny_yolov3_vmss	416x416	5.46	300x0.5	1040.8
61	unet_chaos-CT_pt	512x512	23.3	300x0.5	132.5
62	vgg_16_tf	224x224	31	300x0.5	188.6
63	vgg_19_tf	224x224	39.3	300x0.5	157.5
64	vpgnet_pruned_0_99	480x640	2.5	300x0.5	729.8
65	yolov2_voc	448x448	34	300x0.5	202.9
66	yolov2_voc_pruned_0_66	448x448	11.6	300x0.5	499.6
67	yolov2_voc_pruned_0_71	448x448	9.9	300x0.5	582.3
68	yolov2_voc_pruned_0_77	448x448	7.8	300x0.5	694.9
69	yolov3_adas_pruned_0_9	256x512	5.5	300x0.5	817.1
70	yolov3_bdd	288x512	53.7	300x0.5	94.0
71	yolov3_voc	416x416	65.4	300x0.5	97.3
72	yolov3_voc_tf	416x416	65.6	300x0.5	97.2
73	yolov4_leaky_spp_m	416x416	60.1	300x0.5	100.3
74	yolov4_leaky_spp_m_pruned_0_36	416x416	38.2	300x0.5	123.5

U280 Performance with 2E250 MHz DPUCAHX8L

Refer to the following table for the throughput performance (in frames/sec or fps) for various neural network samples on U280 with DPUCAHX8L running at 2E@250 MHz.

Table 2. U280 Performance with 2E250 MHz DPUCAHX8L
No	Neural Network	Input Size	GOPS	DPU Frequency (MHz)	Performance (fps) (Multiple thread)
1	ENet_cityscapes_pt	512x1024	8.6	250	8.1
2	face_landmark	96x72	0.14	250	9381.2
3	face-quality	80x60	0.06	250	13875.5
4	face-quality_pt	80x60	0.06	250	13837.3
5	facerec_resnet20	112x96	3.5	250	477.2
6	facerec-resnet20_mixed_pt	112x96	3.5	250	475.5
7	facerec_resnet64	112x96	11	250	228.3
8	facereid-small_pt	80x80	0.09	250	8875.4
9	fpn	256x512	8.9	250	49.9
10	FPN_Res18_Medical_segmentation	320x320	45.3	250	16.5
11	FPN-resnet18_covid19-seg_pt	352x352	22.7	250	128.5
12	inception_resnet_v2_tf	299x299	26.4	250	63.9
13	inception_v1	224x224	3.2	250	640.9
14	inception_v1_tf	224x224	3	250	651.9
15	inception_v2	224x224	3.88	250	339.1
16	inception_v3	299x299	11.4	250	188.5
17	inception_v3_pt	299x299	5.7	250	188.0
18	inception_v3_tf	299x299	11.5	250	188.9
19	inception_v3_tf2	299x299	11.5	250	180.0
20	inception_v4	299x299	24.5	250	102.7
21	inception_v4_2016_09_09_tf	299x299	24.6	250	102.8
22	medical_seg_cell_tf2	128x128	5.3	250	136.5
23	MLPerf_resnet50_v1.5_tf	224x224	8.19	250	161.8
24	mlperf_ssd_resnet34_tf	1200x1200	433	250	12.4
25	mobilenet_1_0_224_tf2	224x224	1.1	250	3426.1
26	mobilenet_v1_0_5_160_tf	160x160	0.15	250	9877.1
27	mobilenet_v1_1_0_224_tf	224x224	1.1	250	3485.4
28	mobilenet_v2	224x224	0.6	250	2042.4
29	mobilenet_v2_1_0_224_tf	224x224	0.6	250	2038.4
30	mobilenet_v2_1_4_224_tf	224x224	1.2	250	1493.0
31	multi_task	288x512	14.8	250	25.7
32	openpose_pruned_0_3	368x368	49.9	250	28.0
33	personreid-res50_pt	256x128	5.4	250	186.6
34	plate_detection	320x320	0.49	250	2277.9
35	rcan_pruned_tf	360x640	86.95	250	7.7
36	refinedet_baseline	480x360	123	250	51.9
37	RefineDet-Medical_EDD_tf	320x320	9.8	250	244.6
38	refinedet_pruned_0_8	360x480	25	250	111.9
39	refinedet_pruned_0_92	360x480	10.1	250	130.7
40	refinedet_pruned_0_96	360x480	5.1	250	156.1
41	refinedet_VOC_tf	320x320	81.9	250	79.2
42	reid	80x160	0.95	250	922.6
43	resnet18	224x224	3.7	250	542.0
44	resnet50	224x224	7.7	250	155.6
45	resnet50_pt	224x224	4.1	250	156.8
46	resnet50_tf2	224x224	7.7	250	155.5
47	resnet_v1_101_tf	224x224	14.4	250	102.0
48	resnet_v1_152_tf	224x224	21.8	250	69.3
49	resnet_v1_50_tf	224x224	7	250	178.6
50	retinaface	360x640	1.11	250	278.4
51	salsanext_pt	64x2048	20.4	250	19.3
52	SemanticFPN_cityscapes_pt	256x512	10	250	43.6
53	SemanticFPN_Mobilenetv2_pt	512x1024	5.4	250	12.7
54	semantic_seg_citys_tf2	512x1024	54	250	7.7
55	sp_net	128x224	0.55	250	12681.8
56	squeezenet	227x227	0.76	250	1541.1
57	squeezenet_pt	224x224	0.82	250	942.5
58	ssd_adas_pruned_0_95	360x480	6.3	250	200.3
59	ssdlite_mobilenet_v2_coco_tf	300x300	1.5	250	993.4
60	ssd_mobilenet_v1_coco_tf	300x300	2.5	250	1559.3
61	ssd_mobilenet_v2	360x480	6.6	250	230.7
62	ssd_mobilenet_v2_coco_tf	300x300	3.8	250	374.0
63	ssd_pedestrian_pruned_0_97	360x360	5.9	250	189.1
64	ssd_traffic_pruned_0_9	360x480	11.6	250	201.7
65	vgg_16_tf	224x224	31	250	106.2
66	vgg_19_tf	224x224	39.3	250	93.7
67	vpgnet_pruned_0_99	480x640	2.5	250	114.5