We have detected your current browser version is not the latest one. Xilinx.com uses the latest web technologies to bring you the best online experience possible. Please upgrade to a Xilinx.com supported browser:Chrome, Firefox, Internet Explorer 11, Safari. Thank you!

New PYNQ Jupyter Notebook demonstrates Neural Network Transfer Learning using Astronomical Images of Merging Galaxies

by Xilinx Employee ‎12-05-2017 11:02 AM - edited ‎12-05-2017 11:04 AM (30,317 Views)


Good machine learning heavily depends on large training-data sets, which are not always available. There’s a solution to this problem called transfer learning, which allows the new neural network to leverage an already trained neural network as a starting point. Kaan Kara at ETH Zurich has published an example of transfer learning as a Jupyter Notebook for the Zynq-and-Python based PYNQ development environment on Github. This demo uses the ZipML-PYNQ overlay and analyzes astronomical images of galaxies and puts the images into one of two classes: one showing images of merging galaxies and one that doesn’t.


The work is discussed further in a paper presented at the IEEE 25th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM), 2017. The paper is titled “FPGA-Accelerated Dense Linear Machine Learning: A Precision-Convergence Trade-Off.”