Overview
- 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
Part of the book series: Springer Theses (Springer Theses)
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Table of contents (7 chapters)
Keywords
About this book
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.
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Bibliographic Information
Book Title: Application of FPGA to Real‐Time Machine Learning
Book Subtitle: Hardware Reservoir Computers and Software Image Processing
Authors: Piotr Antonik
Series Title: Springer Theses
DOI: https://doi.org/10.1007/978-3-319-91053-6
Publisher: Springer Cham
eBook Packages: Physics and Astronomy, Physics and Astronomy (R0)
Copyright Information: Springer International Publishing AG, part of Springer Nature 2018
Hardcover ISBN: 978-3-319-91052-9Published: 31 May 2018
Softcover ISBN: 978-3-030-08164-5Published: 10 January 2019
eBook ISBN: 978-3-319-91053-6Published: 18 May 2018
Series ISSN: 2190-5053
Series E-ISSN: 2190-5061
Edition Number: 1
Number of Pages: XXII, 171
Number of Illustrations: 60 b/w illustrations, 8 illustrations in colour
Topics: Optics, Lasers, Photonics, Optical Devices, Image Processing and Computer Vision, Computational Intelligence, Artificial Intelligence