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Application of FPGA to real-time mac...

Antonik, Piotr.

Application of FPGA to real-time machine learninghardware reservoir computers and software image processing /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Application of FPGA to real-time machine learningby Piotr Antonik.
其他題名:	hardware reservoir computers and software image processing /
作者:	Antonik, Piotr.
出版者:	Cham :Springer International Publishing :2018.
面頁冊數:	xxii, 171 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
標題:	Machine learning.
電子資源:	http://dx.doi.org/10.1007/978-3-319-91053-6
ISBN:	9783319910536$q(electronic bk.)

Application of FPGA to real-time machine learninghardware 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$q(electronic bk.)

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

188639
Machine learning.

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

Dewey Class. No.: 006.31

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