DPU on PYNQ

This repository holds the PYNQ DPU overlay. Specifically, the Vitis AI DPU is included in the accompanying bitstreams with example training and inference notebooks ready to run on PYNQ enabled platforms. Steps are also included to rebuild the designs in Vitis and can be ported onto PYNQ-enabled Zynq Ultrascale+ boards.

In this repository, we currently support the following boards:

• Ultra96
• ZCU104
• ZCU111

Other Zynq Ultrascale+ boards may be supported with few adjustments. This repository supports Vitis AI 1.2.

Quick Start

1. Upgrading the PYNQ v2.6 image

This upgrade step is to make sure users have a DPU-ready image. This step is only required for one time.

On your board, run su to use super user. Then run the following commands:

git clone --recursive --shallow-submodules https://github.com/Xilinx/DPU-PYNQ.git
cd DPU-PYNQ/upgrade
make

The upgrade process may take up to 1 hour, since a few packages will need to be installed. Please be patient. For more information, users can check the PYNQ v2.6 upgrade instructions

2. Install

Run the following on board:

pip3 install pynq-dpu

Then go to your jupyter notebook home folder and fetch the notebooks:

cd $PYNQ_JUPYTER_NOTEBOOKS
pynq get-notebooks pynq-dpu -p .

This will make sure the desired notebooks shows up in your jupyter notebook folder.

3. Run

You are ready to go! Now in jupyter, you can explore the notebooks in pynq-dpu folder.

Rebuild DPU Block Design

The DPU IP comes from the Vitis Ai Github. If you want to rebuild the hardware project, you can refer to the instructions for DPU Hardware Design.

In short, the following files will be generated in boards/<Board> folder:

1. dpu.bit
2. dpu.hwh
3. dpu.xclbin

These are the overlay files that can be used by the pynq_dpu package.

Rebuild DPU Models

DPU models for ZCU104 are available on the Vitis AI GitHub repository.

If you want to rebuild the DPU models, you can refer to the instructions for DPU models.

Copyright (C) 2020 Xilinx, Inc