The GO-PYNQ repository is no longer supported.

“GO PYNQ”

• Contest Sponsored by Xilinx

Moto

• To build an AI, Machine Learning, Internet of Things targeting application on Ultra96 or PYNQ-Z2 kit using open-source PYNQ framework.

Application Choices - Using PYNQ

Depends on your creativity, e.g. IoT, Video Processing, Any compute heavy workload being accelerated.

Prizes

Guidelines

Stage 1

• Submission of Abstract with detailed description of the application including proposed technical approach to develop it. Description must include potential kit selection (either Ultra96 or Pynq-Z2 Kit) with a short justification. Top abstracts will qualify to Stage 2 and receive Ultra96/Pynq-Z2 Kits for actual implementation.

Stage 2

• Demo Video Submission of 2 minute of final application , source files submissions along with a final report. Winners will be selected based on actual implementation and application complexity.

Required Skills

• Creative Problem Identification
• Python (working knowledge)
• Domain Knowledge (of the problem space identified)
• Hardware Knowledge (to be able to understand hardware resources available on PYNQ)

Schedule

• Last date for registration - 5th November
• Last date for Abstract submission - 5th November (Hence it is advised to start working as soon as you register)
• Results and Shortlisting of teams for Stage 2 - 15th November
• Last date for submission of Demo Video Report - 20th December
• Final Results - 3rd January

Judging Criteria

Support

• PYNQ being open sourced project of Xilinx, much of the information is available online: http://www.pynq.io/
• Checkout the Presentations and Quickstart sections in this repo
• One pre-determined slot one hour per week, given by Xilinx Employee for support/brainstorming