Following robotics kit and components were used to assemble the PYNQ-BOT. The Arduino board in robotics kit was replaced by PYNQ-Z1 board and Wifi module was replaced by Raspberry Pi USB WiFi Adapter.

2020年9月14日 · PYNQ开源框架的理念与ROS类似，通过大量的Python开源库，以及已经优化的开源overlay，简化如Zynq这样的软硬件协同设计难度与复杂度。 Zynq平台既有处理器，又有FPGA逻辑其架构非常适合在机器人，工业视觉，工业控制的应用。

2024年2月20日 · The PYNQ embedded community page highlights examples of projects for Zynq based boards. Examples include image and video processing, robot and industrial control, machine learning, RISC-V prototyping, RFSoC QPSK and more.

2023年6月30日 · We are thrilled to announce that in the new video of AMD is shown a small demo software for Facial Emotion Classification, made by my company MakarenaLabs, for enabling machine learning, AI, and robotics with PYNQ using our product MuseBox:

2020年9月1日 · PYNQ rocks! Using widgets and AsyncIO we can control complex hardware very easily. Let's use a robot arm to demonstrate. Find this and other hardware projects on Hackster.io.

2021年3月6日 · PYNQ 是 Xilinx 推出的一个开源项目，目的是使用 Python 开发 Xilinx 平台更加容易。 使用 Python 语言和库，设计人员可以利用 Xilinx SOC 器件如 ZYNQ 的可编程逻辑 PL 和微处理器 PS 的优势来构建功能更强大的电子系统。 PYNQ 可与 Zynq、Zynq UltraScale +、Zynq RFSoC、Alveo 加速卡以及 AWS-F1 一起使用，以创建高性能应用程序，如并行硬件执行、高帧率视频处理、硬件加速算法以及实时信号处理等。 很多人会认为 PYNQ 是一款开发板，比如 …

This repository contains the install scripts needed to add PYNQ to the official Ubuntu SDCard Image of your: Kria KV260 Vision AI Starter Kit; Kria KR260 Robotic Starter Kit; Kria KD240 Drives Starter Kit

2023年5月8日 · We’d like to share our tutorial about PYNQ using PYNQ-ZU board. Go through this tutorial, you will learn how to: Examine IPython on Jupyter Lab, and PS processing. Implement accelerated computer vision functions using Vitis Vision Library, Vitis HLS and Vivado.

A self-driving robot car development kit for PYNQ-Z2. This repo contains all the development source including the overlay design and all the embedded projects. A release version contains the pip install package for Robot Car Development Platform for …

2024年5月21日 · We present an overview of the system, and a Python framework to use it on a Pynq ZU platform. We validate the architecture and implementation in the new scenario of robotic arm control, and show how the simulated accuracy is preserved with a peak performance of 3.8M events processed per second.