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AMD Vitis™ Unified Software Platform includes an extensive set of open-source, performance-optimized libraries that offer out-of-the-box acceleration with minimal to zero code changes to your existing applications.
Users who still require these deprecated libraries can continue to access and use previous versions from earlier releases. AI Engine Vitis Libraries are not impacted.
* Also starting with the 2025.2 release, some Alveo platforms will no longer be supported, including U200, U250, and U280. Users can continue using other Alveo platforms (including U50, U50LV, and U55C) to implement PL designs with similar performances.
Use Vitis accelerated libraries in commonly-used programming languages, such as C, C++, Python, and Matlab.
Leverage Vitis platforms as an enabler in your applications—work at the application level and focus your core competencies on solving challenging problems in your domain, accelerate time to insight, and innovate.
Whether you want to accelerate portions of your existing x86 host application code or want to develop accelerators for deployment on AMD embedded platforms, calling a Vitis accelerated library API or kernel in your code offers the same level of abstraction as any software library.

Vitis accelerated libraries are accessible to all developers through GitHub and are scalable across all AMD platforms. Develop your applications using these optimized libraries and seamlessly deploy across AMD platforms at the edge, on-premise, or in the cloud, without having to reimplement your accelerated application.
For rapid prototyping and quick evaluation of the benefits AMD can bring to your applications, you can use them as plug-and-play accelerators, called directly as an API in the user application for several workloads (such as Computer Vision and Image Processing, Quantitative Finance, Database, and Data Analytics, among others).

To design custom accelerators for your application, use Vitis library functions as optimized algorithmic building blocks, modify them to suit your specific needs, or use them as a reference to design your own. You have the flexibility to choose what works best for you.
Combine domain-specific Vitis libraries with pre-optimized deep learning models from the Vitis AI library or the Vitis AI development kit to accelerate your whole application and meet the overall system-level functionality and performance goals.

To report any issues or request support, post in the Vitis section of the Adaptive SoC & FPGA Community Forums.
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$ claude mcp add Vitis_Libraries \
-- python -m otcore.mcp_server <graph>