# Xilinx/PYNQ

Python Productivity for ZYNQ

Repository: https://github.com/Xilinx/PYNQ
Canonical: https://ross.abutalabs.com/products/pynq
Homepage: http://www.pynq.io/
Language: Jupyter Notebook
License: BSD-3-Clause
License Family: permissive
Topics: pynq
Last push: 2026-08-26T15:04:48+00:00

## Health v2 (maintenance only)
Score: 78/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 38, longevity 100
- inputs: {"age_days": 3879, "days_push": 7, "days_rel": 337, "gap_med": 31.0, "n_releases_24m": 3}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2339, forks 861 (observed 2026-08-28T04:06:38.602287+00:00)

## What it is
PYNQ is an open-source Python framework from AMD/Xilinx for designing embedded systems on Zynq and other adaptive computing platforms (FPGA SoCs). It provides a bootable Linux image with Jupyter Notebooks and a Python package so developers can control programmable logic, hardware overlays, and accelerators without traditional hardware design tools.

## Use cases
- accelerate algorithms with FPGA hardware from Python
- build real-time video processing on a Zynq board
- prototype embedded systems with Jupyter notebooks
- teach FPGA and hardware-software co-design to students
- run high frame-rate image processing on programmable logic
- control low-latency IO and signal processing from Python
- deploy hardware overlays on Kria, Alveo, or AWS-F1

## When to choose
- you want to use FPGA programmable logic from Python without writing HDL toolflows
- you need rapid prototyping of hardware-accelerated embedded applications
- you are teaching or learning embedded systems and hardware acceleration
- you target supported Zynq, Kria, Alveo, or AWS-F1 boards

## When to avoid
- your project targets non-Xilinx/AMD FPGA platforms
- you need bare-metal or RTOS-level control without a Linux image
- you require pure software deployment with no programmable hardware
- you need a lightweight library without board-specific overlays or SD images

## Facets
- artifact type: framework
- maturity: active
- function: embedded, sdk, machine-learning, image-processing, video-processing, audio-processing, developer-tools
- domain: embedded-systems, hardware, developer-tools, education, computer-vision
- platform: embedded, python, iot
- tags: fpga, zynq, jupyter-notebooks, hardware-acceleration, programmable-logic, xilinx, amd, overlays, soc, signal-processing, rapid-prototyping, automation, linux, gpu

## Member repositories
- Xilinx/PYNQ (main) score 78

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:38.602287+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:37:26.670137+00:00, confidence not recorded.
  - readme: https://github.com/Xilinx/PYNQ (fetched 2026-08-28T04:06:38.602287+00:00, sha a3dcf1307ce4)
  - homepage: http://www.pynq.io/ (fetched 2026-08-29T10:18:02.664369+00:00, sha bfe93be6ef8a)
  - registry_pypi: https://pypi.org/pypi/pynq/json (fetched 2026-08-29T10:18:02.673549+00:00, sha 695a65819dc2)
- Data as of 2026-08-30T08:39:29.467469+00:00.