Vitis AI Library 1.0 Release Notes - 1.4.1 English

Vitis AI Library User Guide (UG1354)

Document ID
Release Date
1.4.1 English

This section contains information regarding the features and updates of the Vitis AI Library 1.0 release. This release is the successor of last Xilinx AI SDK v2.0 release.

The Vitis AI Library is a set of high-level libraries and APIs built for efficient AI inference with DPU. It provides an easy-to-use and unified interface by encapsulating many efficient and high-quality neural networks.

Key Features And Enhancements

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

Support for new Vitis AI runtime
The Vitis AI Library is updated to be based on the new Vitis AI Runtime with unified APIs. It also fully supports XRT 2019.2.
New DPU support
Besides DPU for Edge devices, the new AI Library will support new cloud based DPU IPs using the same codes (runtime and models for cloud DPU will not be included in this release).
New TensorFlow model support
There are 21 TensorFlow models supported, which are from the official TensorFlow repository. The pre-compiled models for edge devices are included, while original models are in the updated Model Zoo.
New libraries and demos
There are two new libraries libdpmultitask and libdptfssd which supports multi-task models and SSD models from official TensorFlow repository. There is an updated classification demo that shows the usage of the unified APIs in Vitis AI Library runtime.
New Open Source Library
The libdpbase library is open source in this release, which shows how to use unified APIs in Vitis AI runtime to construct high-level libraries.
New Installation Method
The host side environment adopts docker image installation, which simplifies and unifies the installation process.


  • Vitis AI Library 1.0 has been tested with the following images.
    • xilinx-zcu102-dpu-v2019.2.img
    • xilinx-zcu104-dpu-v2019.2.img
  • For existing Xilinx AI SDK v2.0 users, the library interface remains consistent and the application can be directly ported to the new Vitis AI Library.

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 Application
1 inception_resnet_v2_tf Image Classification
2 inception_v1_tf
3 inception_v3_tf
4 inception_v4_2016_09_09_tf
5 mobilenet_v1_0_25_128_tf
6 mobilenet_v1_0_5_160_tf
7 mobilenet_v1_1_0_224_tf
8 mobilenet_v2_1_0_224_tf
9 mobilenet_v2_1_4_224_tf
10 resnet_v1_101_tf
11 resnet_v1_152_tf
12 resnet_v1_50_tf
13 vgg_16_tf
14 vgg_19_tf
15 ssd_mobilenet_v1_coco_tf Object Detection
16 ssd_mobilenet_v2_coco_tf
17 ssd_resnet_50_fpn_coco_tf
18 yolov3_voc_tf
19 mlperf_ssd_resnet34_tf
20 resnet50 Image Classification
21 resnet18
22 inception_v1
23 inception_v2
24 inception_v3
25 inception_v4
26 mobilenet_v2
27 squeezenet
28 ssd_pedestrian_pruned_0_97 ADAS Pedestrian Detection
29 ssd_traffic_pruned_0_9 Traffic Detection
30 ssd_adas_pruned_0_95 ADAS Vehicle Detection
31 ssd_mobilenet_v2 Object Detection
32 refinedet_pruned_0_8
33 refinedet_pruned_0_92
34 refinedet_pruned_0_96
35 vpgnet_pruned_0_99 ADAS Lane Detection
36 fpn ADAS Segmentation
37 sp_net Pose Estimation
38 openpose_pruned_0_3  
39 densebox_320_320 Face Detection
40 densebox_640_360
41 face_landmark Face Detection and Recognition
42 reid Object tracking
43 multi_task ADAS
44 yolov3_adas_pruned_0_9 Object Detection
45 yolov3_voc
46 yolov3_bdd
47 yolov2_voc
48 yolov2_voc_pruned_0_66
49 yolov2_voc_pruned_0_71
50 yolov2_voc_pruned_0_77
  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 1.0.

Table 2. Device Support
Platform EVB Version
Zynq UltraScale+ MPSoC ZU9EG Xilinx ZCU102 V1.1
Zynq® UltraScale+™ MPSoC ZU7EV Xilinx ZCU104 V1.0


Because of the complicated configuration for SSD models from the official TensorFlow repository, there is a new libdptfssd library that is different from the original libdpssd library for Caffe models. These two libraries may be merged in future releases.

Deprecated Features

The following features are deprecated in Vitis AI Library 1.0:

Removed demos
The squeezenet and SSD demos have been removed. Because we highly encourage customers to use high-level APIs from AI Library for applications and solutions, we only provide one classification demo in this release to show how to use the low-level unified APIs in Vitis AI run time.
Removed pre-compiled models
Six TensorFlow models have been removed in this release but provided in the previous Xilinx AI v2.0 release, to keep sync with the updated Model Zoo. Models that are removed can be replaced by similar models in updated the Model Zoo which come from TensorFlow slim models. The models are:
  • resnet_50_tf
  • inception_v1_tf
  • resnet_18_tf
  • mobilenet_v1_tf
  • mobilenet_v2_tf
  • ssd_voc_tf