Vision AI DPU-PYNQ

by: AMD

PYNQ is an open-source productivity framework built with Python, Jupyter, and an extensive ecosystem of associated libraries. It increases the productivity of software and hardware engineers by using the Zynq™ family of devices to build more capable and intelligent systems. The DPU-PYNQ Accelerated Application includes a Vitis™ AI DPU (Deep Learning Processor Unit) with AI inference notebooks ready to run out of the box.

Vision AI DPU-PYNQ

Features:

  • PYNQ is an open-source python framework from AMD​
  • PYNQ is built for developers who want to maximize the capabilities of Kria™ SOMs but have limited K24 expertise​
  • Using the Python language and libraries, designers can leverage the programmable logic (PL) to build more capable and innovative target applications
  • The DPU-PYNQ overlay includes Vitis AI DPU with Jupyter Notebooks for AI inference 

Hardware Needed:

Frequently Asked Questions

Visit the PYNQ website to learn more about PYNQ, other boards that are supported, community projects, and for support.

Featured Documents
Powering Electric Drive Control & Efficiency with Adaptive Computing
Powering Electric Drive Control & Efficiency with Adaptive Computing

Kria™ adaptive System-on-Module (SOM) devices from AMD play an important role in electric drive control. They can optimize performance, help a motor run more efficiently, reduce power consumption, mitigate noise, cut vibration, and detect potential failures before they happen. Download our new motor control eBook to learn more!

Accelerate Your AI-Enabled Edge Solution with Adaptive Computing
Accelerate Your AI-Enabled Edge Solution with Adaptive Computing

Learn all about adaptive SOMs, including examples of why and how they can be deployed in next-generation edge applications, and how smart vision providers benefit from the performance, flexibility, and rapid development that can only be achieved by an adaptive SOM.

Adaptive Computing in Robotics
Adaptive Computing in Robotics

Demand for robotics is accelerating rapidly. Building a robot that is designed to be safe and secure and can operate alongside humans is difficult enough. But getting these technologies working together can be even more challenging. Complicating matters is the addition of machine learning and artificial intelligence, which is making it more difficult to keep up with computational demands.

Roboticists are turning toward adaptive computing platforms, which offer lower latency and deterministic, multi-axis control with built-in safety and security features on an integrated, adaptable platform that is expandable for the future. Read the eBook to learn more.