| 期刊全稱 | Application of FPGA to Real‐Time Machine Learning | | 期刊簡(jiǎn)稱 | Hardware Reservoir C | | 影響因子2023 | Piotr Antonik | | 視頻video | http://file.papertrans.cn/160/159110/159110.mp4 | | 發(fā)行地址 | 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 | | 學(xué)科分類 | Springer Theses | | 圖書封面 |  | | 影響因子 | 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 tomograp | | Pindex | Book 2018 |
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