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Application of FPGA to Real‐Time Machine Learning

Hardware Reservoir Computers and Software Image Processing

  • Piotr Antonik

Part of the Springer Theses book series (Springer Theses)

Table of contents

  1. Front Matter
    Pages i-xxii
  2. Piotr Antonik
    Pages 1-37
  3. Piotr Antonik
    Pages 63-89
  4. Piotr Antonik
    Pages 123-135
  5. Piotr Antonik
    Pages 161-166
  6. Back Matter
    Pages 167-171

About this book

Introduction

This book lies at the interface of machine learning – a subfield of computer science that develops algorithms for challenging tasks such as shape or image recognition, where traditional algorithms fail – and photonics – the physical science of light, which underlies many of the optical communications technologies used in our information society. It provides a thorough introduction to reservoir computing and field-programmable gate arrays (FPGAs).

Recently, photonic implementations of reservoir computing (a machine learning algorithm based on artificial neural networks) have made a breakthrough in optical computing possible. In this book, the author pushes the performance of these systems significantly beyond what was achieved before. By interfacing a photonic reservoir computer with a high-speed electronic device (an FPGA), the author successfully interacts with the reservoir computer in real time, allowing him to considerably expand its capabilities and range of possible applications. Furthermore, the author draws on his expertise in machine learning and FPGA programming to make progress on a very different problem, namely the real-time image analysis of optical coherence tomography for atherosclerotic arteries.

Keywords

Photonics Opto-electronics Artificial Intelligence Hardware Artificial Neural Networks Photonic Reservoir Computing Field-programmable Gate Array Biomedical Imaging Online Learning Telecommunications Prediction of Chaotic Time Series Reservoir computing FPGAs

Authors and affiliations

  • Piotr Antonik
    • 1
  1. 1.CentraleSupélecMetzFrance

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-91053-6
  • Copyright Information Springer International Publishing AG, part of Springer Nature 2018
  • Publisher Name Springer, Cham
  • eBook Packages Physics and Astronomy
  • Print ISBN 978-3-319-91052-9
  • Online ISBN 978-3-319-91053-6
  • Series Print ISSN 2190-5053
  • Series Online ISSN 2190-5061
  • Buy this book on publisher's site
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