Vitis AI is Xilinx’s development stack for AI inference on Xilinx hardware platforms, including both edge devices and Alveo cards. It consists of optimized IP, tools, libraries, models, and example designs. It is designed with high efficiency and ease of use in mind, unleashing the full potential of AI acceleration on Xilinx FPGA and ACAP.

Vitis AI is composed of the following key components:

• AI Model Zoo - A comprehensive set of pre-optimized models that are ready to deploy on Xilinx devices.
• AI Optimizer - An optional model optimizer that can prune a model by up to 90%. It is separately available with commercial licenses.
• AI Quantizer - A powerful quantizer that supports model quantization, calibration, and fine tuning.
• AI Compiler - Compiles the quantized model to a high-efficient instruction set and data flow.
• AI Profiler - Perform an in-depth analysis of the efficiency and utilization of AI inference implementation.
• AI Library - Offers high-level yet optimized C++ APIs for AI applications from edge to cloud.
• DPU - Efficient and scalable IP cores can be customized to meet the needs for many different applications

Learn More: Vitis AI Overview

See What's New

• Release Notes
• Alveo U50 support with DPUv3E, a throughput optimized CNN overlay
• Tensorflow 1.15 support
• VART (Vitis AI Runtime) with unified API and samples for Zynq, ZU+ and Alveo
• Vitis AI library fully open source
• Whole Application Acceleration example on Alveo

Getting Started

Two options are available for installing the containers with the Vitis AI tools and resources.

• Pre-built containers on Docker Hub: /xilinx/vitis-ai
• Build containers locally with Docker recipes: Docker Recipes

Installation

• Install Docker - if Docker not installed on your machine yet

• Ensure your linux user is in the group docker

• Clone the Vitis-AI repository to obtain the examples, reference code, and scripts.

  git clone --recurse-submodules https://github.com/Xilinx/Vitis-AI  
  
  cd Vitis-AI
  

• Run Docker Container

  1. Run the CPU image from docker hub

  ./docker_run.sh xilinx/vitis-ai
  

  or

  2. build the CPU image locally and run it

  cd docker
  ./docker_build_cpu.sh
  
  # After build finished
  cd ..
  ./docker_run.sh xilinx/vitis-ai-cpu:latest
  

  or

  3. build the GPU image locally and run it

  cd docker
  ./docker_build_gpu.sh
  
  # After build finished
  cd ..
  ./docker_run.sh xilinx/vitis-ai-gpu:latest
  

• Get started with examples

  • VART samples
  • Alveo
  • Vitis AI DNNDK samples

Programming with Vitis AI

Vitis AI offers a unified set of high-level C++/Python programming APIs to run AI applications across edge-to-cloud platforms, including DPUv1 and DPUv3 for Alveo,
and DPUv2 for Zynq Ultrascale+ MPSoC and Zynq-7000. It brings the benefits to easily port AI applications from cloud to edge and vice versa. 7 samples in VART Samples are available to help you get familiar with the unfied programming APIs.

ID	Example Name	Models	Framework	Notes
1	resnet50	ResNet50	Caffe	Image classification with VART C++ APIs.
2	resnet50_mt_py	ResNet50	TensorFlow	Multi-threading image classification with VART Python APIs.
3	inception_v1_mt_py	Inception-v1	TensorFlow	Multi-threading image classification with VART Python APIs.
4	pose_detection	SSD, Pose detection	Caffe	Pose detection with VART C++ APIs.
5	video_analysis	SSD	Caffe	Traffic detection with VART C++ APIs.
6	adas_detection	YOLO-v3	Caffe	ADAS detection with VART C++ APIs.
7	segmentation	FPN	Caffe	Semantic segmentation with VART C++ APIs.

For more information, please refer to Vitis AI User Guide

References

• Vitis AI Overview
• Vitis AI User Guide
• Vitis AI Model Zoo with Performance & Accuracy Data
• Vitis AI Tutorials
• Developer Articles
• Performance Whitepaper

System Requirements

Questions and Support

• FAQ
• Vitis AI Forum