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Vitis AI

Optimal Artificial Intelligence Inference from Edge to Cloud

Coming in November


Powering Artificial Intelligence and Deep Learning

Vitis™ AI is Xilinx’s development platform 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 Deployment Features

How your development works with AI:

  • Supports mainstream frameworks and the latest models capable of diverse deep learning tasks
  • Provides a comprehensive set of pre-optimized models that are ready to deploy on Xilinx devices. You can find the closest model and start re-training for your applications!
  • Provides a powerful quantizer that supports model quantization, calibration, and fine tuning. For advanced users, we also offer an optional AI optimizer that can prune a model by up to 90%
  • The AI profiler provides layer by layer analysis to help with bottlenecks
  • The AI library offers high-level C++ and Python APIs for maximum portability from edge to cloud.
  • Efficient and scalable IP cores can be customized to meet your needs for many different applications from a throughput, latency, and power perspective

Explore All the Possibilities with Vitis AI


AI Optimizer

With world-leading model compression technology, we can reduce model complexity by 5x to 50x with minimal accuracy impact. Deep Compression takes the performance of your AI inference to the next level.

Artificial Intelligence Optimizer Block Diagram

Artificial Intelligence Quantizer Block Diagram

AI Quantizer

By converting the 32-bit floating-point weights and activations to fixed-point like INT8, the AI Quantizer can reduce the computing complexity without losing prediction accuracy. The fixed-point network model requires less memory bandwidth, thus providing faster speed and higher power efficiency than the floating-point model.

AI Compiler

Maps the AI model to a high-efficient instruction set and data flow. Also performs sophisticated optimizations such as layer fusion, instruction scheduling, and reuses on-chip memory as much as possible.

Artificial Intelligence Compiler Block Diagram

AI Profiler

The performance profiler allows programmers to perform an in-depth analysis of the efficiency and utilization of your AI inference implementation.

AI Library

The runtime provides a lightweight set of C++ and Python APIs. enabling easy application development. It also provides efficient task scheduling, memory management, and interrupt handling.

Artificial Intelligence Library Block Diagram

Vitis AI Model Zoo

Vitis AI model zoo includes optimized deep learning models to speed up the deployment of deep learning inference on Xilinx™ platforms. These models cover different applications, including but not limited to ADAS/AD, video surveillance, robotics, data center, etc. You can get started with these pre-trained models to enjoy the benefits of deep learning acceleration.

AI Model Zoo


The Xilinx AI Platform supports many AI/ML Models supported, as shown below. We are continually working to bring the latest models into our platform.

Application Task Algorithm
General Image classification Googlenetv1, Resnet50, Resnet101, Resnet152 Inception v1, BN-inception, VGG16, SqueezeNet, Mobilenet , MobilenetV2
Object Detection MobilnetV2-SSD, SSD, YOLO v2, YOLO v3, Tiny YOLO v2, Tiny YOLO v3
Segmentation ENet, ESPNet
 Face Face detection SSD, Densebox
Landmark Localization Coordinates Regression
Face recognition ResNet + Triplet / A-softmax Loss
Face attributes recognition Classification and regression

Pedestrian Detection SSD
Pose Estimation Coordinates Regression

Video Analytics
Object detection SSD, RefineDet
Pedestrian Attributes Recognition GoogleNet
Car Attributes Recognition GoogleNet
Car Logo Recognition Modified Densebox + GoogleNet
License Plate Detection Modified DenseBox
License Plate Recognition GoogleNet + Multi-task Learning

Object Detection SSD, YOLOv2, YOLOv3
Lane Detection VPGNet
Semantic Segmentation FPN
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