/pynq-juliabrot

Primary LanguagePythonBSD 3-Clause "New" or "Revised" LicenseBSD-3-Clause

PYNQ Juliabrot Fractal Factory

A Xilinx FPGA accelerated near real-time interactive Python Jupyter Lab Notebook

Compatible with the Avnet PYNQ ZUBoard 1CG, Avnet PYNQ Ultra96 v1/v2 and Xilinx PYNQ Z1/Z2 development boards

To demonstrate potential, several full-rez up to 16K sized images are available: click here

The U96 board execution is faster than the ZUBoard 1CG & Z1/Z2 but otherwise the features are the same for both platforms.

fractal1 fractal2

fractal3 zoomit

Setup and installation:

  • Install PYNQ v3.0.1 or newer on your ZUBoard 1CG, Ultra96 or PYNQ Z1/Z2 board

  • (For PYNQ Z1/Z2 board only), open a PYNQ board console:

    • Use ssh xilinx@<your board IP address> (default password 'xilinx') OR
    • Open Jupyter Lab's root console

      • http://<your board IP address>/lab

      • If requested, enter xilinx for the default password

      • Click the Terminal Icon to open a console

      • Enter the commands below in the console to complete the installation, installing ipycanvas will take a while.

        sudo pip3 install ipycanvas

      • Next, install the notebook itself

        cd $PYNQ_JUPYTER_NOTEBOOKS
git clone https://github.com/FredKellerman/pynq-juliabrot
cd pynq-juliabrot
git checkout origin/master

  • If not already open, in your browser go to http://<your board IP address>/lab

  • Use the Jupyter Lab Folder/File Explorer and under the folder pynq-juliabrot open juliabrot-zoom.ipynb

  • Execute the notebook

  • After executing jui.draw_roaming_ui() you should see:

gui

  • Use mouse click to start selection, click again to compute area within selection
  • Enjoy!

interface

Mandelbrot / Julia FPGA Compute Engine Attributes

Up to 16K x 16K grid sizes
Max Iterations up to 4,294,967,296

juliabrot Overlay Precision N Kernels Logic MHz # DSP48s
96b 64-bits 6 300MHz 300
96b_mid 95-bits 4 300MHz 320
96b_deep 160-bits 1 214MHz 248
z1 (z2 also) 64-bits 3 125MHz 150
ZUBoard 1CG 90-bits 3 250MHz 216

Note: at this time the Python front end used for initial conditions only supports up to 80-bit precision, future plans are to remove this limitation. Due to dev board memory limitations, images larger than 4K for coloring and formating should be completed off target on a PC.

The author would like to thank Github users @francof2a, @martinRenou for their kind replies and awesome contributions to the Open Source community.

https://github.com/martinRenou/ipycanvas

https://github.com/francof2a/fxpmath