Vitis AI Library 1.1 Release Notes

This section contains information regarding the features and updates of the Vitis™ AI Library 1.1 release.

Key Features And Enhancements

This AI Library release includes the following key features and enhancements:

Support for the Cloud
The Alveo U50 card is supported with this release.
New DPU support
DPU is supported which can be used for the Cloud.
New Open Source Library
The xnnpp library is open source in this release, which shows how to do the pre-processing and post-processing for the neural networks.
Model Library Update
The new model library unifies the interface between the Cloud and Edge devices.

Changes

The installation mode of the host for the Edge is changed, and the original Docker installation mode is no longer used.

Compatibility

The Vitis™ AI Library 1.1 is tested with the following images.

  • xilinx-zcu102-dpu-v2019.2-v2.img
  • xilinx-zcu104-dpu-v2019.2-v2.img

Model Support

The following models are supported by this version of the Vitis™ AI Library.

Table 1. Models Supported by the Vitis AI Library
No. Neural Network ZCU102/ZCU104 U50 Application
1 inception_resnet_v2_tf Y Y Image Classification
2 inception_v1_tf Y Y
3 inception_v3_tf Y Y
4 inception_v4_2016_09_09_tf Y Y
5 mobilenet_v1_0_25_128_tf Y N/A
6 mobilenet_v1_0_5_160_tf Y N/A
7 mobilenet_v1_1_0_224_tf Y N/A
8 mobilenet_v2_1_0_224_tf Y N/A
9 mobilenet_v2_1_4_224_tf Y N/A
10 resnet_v1_101_tf Y Y
11 resnet_v1_152_tf Y Y
12 resnet_v1_50_tf Y Y
13 vgg_16_tf Y Y
14 vgg_19_tf Y Y
15 ssd_mobilenet_v1_coco_tf Y N/A Object Detection
16 ssd_mobilenet_v2_coco_tf Y N/A
17 ssd_resnet_50_fpn_coco_tf Y Y
18 yolov3_voc_tf Y Y
19 mlperf_ssd_resnet34_tf Y N/A
20 resnet50 Y Y Image Classification
21 resnet18 Y Y
22 inception_v1 Y Y
23 inception_v2 Y Y
24 inception_v3 Y Y
25 inception_v4 Y Y
26 mobilenet_v2 Y N/A
27 squeezenet Y Y
28 ssd_pedestrian_pruned_0_97 Y Y ADAS Pedestrian Detection
29 ssd_traffic_pruned_0_9 Y Y Traffic Detection
30 ssd_adas_pruned_0_95 Y Y ADAS Vehicle Detection
31 ssd_mobilenet_v2 Y N/A Object Detection
32 refinedet_pruned_0_8 Y Y
33 refinedet_pruned_0_92 Y Y
34 refinedet_pruned_0_96 Y Y
35 vpgnet_pruned_0_99 Y Y ADAS Lane Detection
36 fpn Y Y ADAS Segmentation
37 sp_net Y Y Pose Estimation
38 openpose_pruned_0_3 Y Y
39 densebox_320_320 Y Y Face Detection
40 densebox_640_360 Y Y
41 face_landmark Y Y Face Detection and Recognition
42 reid Y Y Object tracking
43 multi_task Y Y ADAS
44 yolov3_adas_pruned_0_9 Y Y Object Detection
45 yolov3_voc Y Y
46 yolov3_bdd Y Y
47 yolov2_voc Y Y
48 yolov2_voc_pruned_0_66 Y Y
49 yolov2_voc_pruned_0_71 Y Y
50 yolov2_voc_pruned_0_77 Y Y
  1. No1-No19 neural network models are trained based on the TensorFlow framework.
  2. No20-No50 neural network models are trained based on the Caffe framework.

Device Support

The following platforms and EVBs are supported by the Vitis™ AI Library 1.1.

Table 2. Edge Device Support
Platform EVB Version
Zynq UltraScale+ MPSoC ZU9EG Xilinx ZCU102 V1.1
Zynq® UltraScale+™ MPSoC ZU7EV Xilinx ZCU104 V1.0
Table 3. Cloud Device Support
Accelerator Cards
Xilinx Alveo U50 Data Center Accelerator Card

Limitations

Some neural networks with mobilenet as the backbone are not supported on the Alveo U50 accelerator card.