Application of FPGA to Real‐Time Machine Learning Hardware Reservoir Computers and Software Image Processing
Aus der Reihe
Springer Theses
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- Hardcover
- Taschenbuch ausgewählt
- eBook
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Sprache:Englisch
Fr. 150.00
inkl. gesetzl. MwSt.,
Beschreibung
Produktdetails
Einband
Taschenbuch
Erscheinungsdatum
10.01.2019
Abbildungen
XXII, 68 illus., 8 illus. in color., schwarz-weiss Illustrationen, farbige Illustrationen
Verlag
SpringerSeitenzahl
171
Maße (L/B/H)
23.5/15.5/1.1 cm
Gewicht
306 g
Auflage
Softcover reprint of the original 1st ed. 2018
Sprache
Englisch
ISBN
978-3-030-08164-5
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.
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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