PYNQ is an open-source project from AMD®that makes it easier to use Adaptive Computing platforms.
Using the Python language and libraries, designers can exploit the benefits of programmable logic and microprocessors to build more capable and exciting electronic systems.
PYNQ can be used with Zynq, Zynq UltraScale+, Zynq RFSoC, Alveo accelerator boards and AWS-F1 to create high performance applications with:
parallel hardware execution
high frame-rate video processing
hardware accelerated algorithms
real-time signal processing
high bandwidth IO
low latency control
PYNQ is intended to be used by a wide range of designers and developers including:
Software developers who want to take advantage of the capabilities of Adaptive Computing platforms without having to use ASIC-style design tools to design hardware.
System architects who want an easy software interface and framework for rapid prototyping and development of their Zynq, Alveo and AWS-F1 design.
Hardware designers who want their designs to be used by the widest possible audience.
Jupyter Notebookis a browser based interactive computing environment. Jupyter notebook documents can be created that include live code, interactive widgets, plots, explanatory text, equations, images and video.
A PYNQ enabled board can be easily programmed in Jupyter Notebook using Python.
Using Python, developers can use hardware libraries and overlays on the programmable logic. Hardware libraries, or overlays, can speed up software running on a Zynq or Alveo board, and customize the hardware platform and interfaces.
PYNQ can be delivered in two ways; as a bootable Linux image for a Zynq board, which includes the pynq Python package, and other open-source packages, or as a Python package for Kria, or an Alveo or AWS-F1 host computer.