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• 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

• 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

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

• 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 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

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.

For more information, please refer to Vitis AI User Guide

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

• FAQ
• Vitis AI Forum