Application Of Fpga To Real‐time Machine Learning: Hardware Reservoir Computers And Software Image Processing (springer Theses)
by Piotr Antonik /
2018 / English / PDF
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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).
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.