Xilinx Vitis AI User Guide

This document serves as the comprehensive user guide for the Xilinx Vitis AI development environment. Vitis AI is engineered to accelerate artificial intelligence (AI) inference workloads on Xilinx hardware platforms, encompassing both Edge devices and Alveo accelerator cards. It provides a robust suite of optimized IP cores, development tools, pre-trained models, and example designs.

The Vitis AI platform is designed for high efficiency and ease of use, enabling developers to harness the full potential of AI acceleration on Xilinx FPGAs and Adaptive Compute Acceleration Platforms (ACAPs). It simplifies the development of deep-learning inference applications by abstracting the complexities of the underlying hardware.

Key Components and Features

The Vitis AI environment integrates several critical components to streamline the AI development workflow:

Deep-Learning Processor Unit (DPU): A programmable engine optimized for deep neural networks, available as parameterizable IP cores for efficient hardware implementation.
Framework Support: Compatibility with popular machine learning frameworks such as Caffe, PyTorch, and TensorFlow, allowing for seamless model import and optimization.
Quantization and Optimization: Tools for model quantization, calibration, and fine-tuning, including an optional AI optimizer for advanced model pruning.
High-Level APIs: Unified C++ and Python APIs for maximum portability across various Xilinx platforms, from Edge to Cloud.

For detailed information on integrating the DPU into customized AI projects, users can refer to the DPU-TRD on GitHub.

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Key Components and Features

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