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Application of FPGA to real-time mac...

Antonik, Piotr.

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Application of FPGA to real-time machine learning = hardware reservoir computers and software image processing /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Application of FPGA to real-time machine learning/ by Piotr Antonik.
Reminder of title:	hardware reservoir computers and software image processing /
Author:	Antonik, Piotr.
Published:	Cham :Springer International Publishing : : 2018.,
Description:	xxii, 171 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Introduction -- Online Training of a Photonic Reservoir Computer -- Backpropagation with Photonics -- Photonic Reservoir Computer with Output Feedback -- Towards Online-Trained Analogue Readout Layer -- Real-Time Automated Tissue Characterisation for Intravascular OCT Scans -- Conclusion and Perspectives.
Contained By:	Springer eBooks
Subject:	Machine learning. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-91053-6
ISBN:	9783319910536

Application of FPGA to real-time machine learning = hardware reservoir computers and software image processing /
Antonik, Piotr.

Application of FPGA to real-time machine learninghardware reservoir computers and software image processing /[electronic resource] :by Piotr Antonik. - Cham :Springer International Publishing :2018. - xxii, 171 p. :ill. (some col.), digital ;24 cm. - Springer theses,2190-5053. - Springer theses..

Introduction -- Online Training of a Photonic Reservoir Computer -- Backpropagation with Photonics -- Photonic Reservoir Computer with Output Feedback -- Towards Online-Trained Analogue Readout Layer -- Real-Time Automated Tissue Characterisation for Intravascular OCT Scans -- Conclusion and Perspectives.

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.

ISBN: 9783319910536

Standard No.: 10.1007/978-3-319-91053-6doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5 / .A586 2018

Dewey Class. No.: 006.31

Application of FPGA to real-time machine learning = hardware reservoir computers and software image processing /
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300 $a xxii, 171 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Springer theses, $x 2190-5053
505 0 $a Introduction -- Online Training of a Photonic Reservoir Computer -- Backpropagation with Photonics -- Photonic Reservoir Computer with Output Feedback -- Towards Online-Trained Analogue Readout Layer -- Real-Time Automated Tissue Characterisation for Intravascular OCT Scans -- Conclusion and Perspectives.
520 $a 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.
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