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Vitis AI

Adaptable and Real-Time
AI Inference Acceleration

Overview

Optimal Artificial Intelligence Inference from Edge to Cloud

The Vitis? AI development environment 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.??
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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

Vitis AI Model Zoo

https://github.com/Xilinx/Vitis-AI/tree/master/AI-Model-Zoo

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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
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