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.”
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December 6, 2017 at 04:11AM