Springer Theses

Application of FPGA to Real‐Time Machine Learning

Hardware Reservoir Computers and Software Image Processing

Authors: Antonik, Piotr

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  • Nominated as an outstanding Ph.D. thesis by the Université libre de Bruxelles, Belgium
  • Provides a thorough introduction to reservoir computing and field-programmable gate arrays Discusses the problems encountered on the path to the results discussed Uses an engaging and lively writing style
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eBook $109.00
price for USA in USD (gross)
  • ISBN 978-3-319-91053-6
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $139.99
price for USA in USD
  • ISBN 978-3-319-91052-9
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $139.99
price for USA in USD
  • ISBN 978-3-030-08164-5
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
About this book

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.

About the authors

Piotr Antonik was born in 1989 in Minsk, Belarus. He received his Master's degree and his PhD in physics from the Université libre de Bruxelles, Brussels, Belgium, in 2013 and 2017, respectively. He is currently a post-doctoral researcher at the LMOPS Lab, CentraleSupélec, Metz, France. His  research interests include spatial and time-delay photonic implementations of reservoir computing, FPGA programming, online learning methods, and applications of machine learning to biomedical imaging.

Table of contents (7 chapters)

Table of contents (7 chapters)
  • Introduction

    Pages 1-37

    Antonik, Piotr

  • Online Training of a Photonic Reservoir Computer

    Pages 39-62

    Antonik, Piotr

  • Backpropagation with Photonics

    Pages 63-89

    Antonik, Piotr

  • Photonic Reservoir Computer with Output Feedback

    Pages 91-121

    Antonik, Piotr

  • Towards Online-Trained Analogue Readout Layer

    Pages 123-135

    Antonik, Piotr

Buy this book

eBook $109.00
price for USA in USD (gross)
  • ISBN 978-3-319-91053-6
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $139.99
price for USA in USD
  • ISBN 978-3-319-91052-9
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $139.99
price for USA in USD
  • ISBN 978-3-030-08164-5
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Application of FPGA to Real‐Time Machine Learning
Book Subtitle
Hardware Reservoir Computers and Software Image Processing
Authors
Series Title
Springer Theses
Copyright
2018
Publisher
Springer International Publishing
Copyright Holder
Springer International Publishing AG, part of Springer Nature
eBook ISBN
978-3-319-91053-6
DOI
10.1007/978-3-319-91053-6
Hardcover ISBN
978-3-319-91052-9
Softcover ISBN
978-3-030-08164-5
Series ISSN
2190-5053
Edition Number
1
Number of Pages
XXII, 171
Number of Illustrations
60 b/w illustrations, 8 illustrations in colour
Topics