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Vitis AI Library v2.5

Introduction

The Vitis AI Library is a set of high-level libraries and APIs built for efficient AI inference with Deep-Learning Processor Unit (DPU). It is built based on the Vitis AI Runtime with Unified APIs, and it fully supports XRT 2022.1.

The Vitis AI Library provides an easy-to-use and unified interface by encapsulating many efficient and high-quality neural networks. This simplifies the use of deep-learning neural networks, even for users without knowledge of deep-learning or FPGAs. The Vitis AI Library allows users to focus more on the development of their applications, rather than the underlying hardware.

For edge users, click Quick Start For Edge to get started quickly.

For cloud users, click Quick Start For Cloud to get started quickly.

Directory Structure Introduction

Vitis_AI_Library
├── apps
│   ├── multitask_v3_quad_windows
│   ├── seg_and_pose_detect
│   ├── segs_and_roadline_detect
│   ├── vck190_4mipi
│   └── vck190_4video
├── README.md
└── samples
    ├── 3Dsegmentation
    ├── bcc
    ├── c2d2_lite
    ├── centerpoint
    ├── classification
    ├── clocs
    ├── covid19segmentation
    ├── dpu_task
    ├── efficientdet_d2
    ├── facedetect
    ├── facefeature
    ├── facelandmark
    ├── facequality5pt
    ├── fairmot
    ├── graph_runner
    ├── hourglass
    ├── lanedetect
    ├── medicaldetection
    ├── medicalsegcell
    ├── medicalsegmentation
    ├── multitask
    ├── multitaskv3
    ├── ocr
    ├── ofa_yolo
    ├── openpose
    ├── platedetect
    ├── platenum
    ├── pmg
    ├── pointpainting
    ├── pointpillars
    ├── pointpillars_nuscenes
    ├── polypsegmentation
    ├── posedetect
    ├── rcan
    ├── refinedet
    ├── reid
    ├── retinaface
    ├── RGBDsegmentation
    ├── segmentation
    ├── solo
    ├── ssd
    ├── textmountain
    ├── tfssd
    ├── ultrafast
    ├── vehicleclassification
    ├── yolov2
    ├── yolov3
    ├── yolov4
    └── yolovx

Quick Start For Edge

Setting Up the Host

For MPSOC, follow Setting Up the Host to set up the host for edge.
For VCK190, follow Setting Up the Host to set up the host for edge.

Setting Up the Target

For MPSOC, follow Setting Up the Target to set up the target.
For VCK190, follow Setting Up the Target to set up the target.

Running Vitis AI Library Examples

Follow Running Vitis AI Library Examples to run Vitis AI Library examples.

Note: When you update from VAI1.3 to VAI2.0 and VAI2.5, refer to the following to modify your compilation options.

  1. For Petalinux 2021.1 and above, it uses OpenCV4, and for Petalinux 2020.2, it uses OpenCV3. So set the OPENCV_FLAGS as needed. You can refer to the following.
result=0 && pkg-config --list-all | grep opencv4 && result=1
if [ $result -eq 1 ]; then
	OPENCV_FLAGS=$(pkg-config --cflags --libs-only-L opencv4)
else
	OPENCV_FLAGS=$(pkg-config --cflags --libs-only-L opencv)
fi
  1. Include -lvitis_ai_library-dpu_task in the build script.

Quick Start For Cloud

Setting Up the Host

For demonstration purposes, we provide the following pre-compiled DPU IP with Vitis AI Library Sample support. You can choose one of them according to your own Accelerator Card.

No. Accelerator Card DPU IP
1 U50LV DPUCAHX8H
2 U50LV DPUCAHX8H-DWC
3 U55C DPUCAHX8H-DWC
4 U200 DPUCADF8H
5 U250 DPUCADF8H
6 VCK5000-PROD DPUCVDX8H_4pe_miscdwc
7 VCK5000-PROD DPUCVDX8H_6pe_dwc
8 VCK5000-PROD DPUCVDX8H_6pe_misc
9 VCK5000-PROD DPUCVDX8H_8pe_normal

For U50LV and U55C Alveo Card, follow Setup Alveo Accelerator Card to set up the host.

For U200 and U250 Alveo Card, follow Setup Alveo Accelerator Card to set up the host.

For VCK5000-PROD Versal Card, follow Setup VCK5000 Accelerator Card to set up the host.

Running Vitis AI Library Examples

For U50LV and U55C Alveo Card, refer to Running Vitis AI Library Examples on U50LV/U55C/VCK5000 section of README.

For U200 and U250 Alveo Card, refer to Running Vitis AI Library Examples on Alveo-U200/Alveo-U250 section of README.

For VCK5000-PROD Versal Card, refer to Running Vitis AI Library Examples on U50LV/U55C/VCK5000 section of README.

Reference

For more information, please refer to vitis-ai-library-user-guide.