Application of FPGA to Real‐Time Machine Learning: Hardware Reservoir Computers and Software Image Processing (Springer Theses)
暫譯: FPGA在即時機器學習中的應用：硬體儲水電腦與軟體影像處理 (Springer 論文集)
Piotr Antonik
- 出版商: Springer
- 出版日期: 2018-05-31
- 售價: $4,890
- 貴賓價: 9.5 折 $4,646
- 語言: 英文
- 頁數: 171
- 裝訂: Hardcover
- ISBN: 3319910523
- ISBN-13: 9783319910529
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相關分類:
FPGA、Machine Learning
海外代購書籍(需單獨結帳)
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相關主題
商品描述
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.
商品描述(中文翻譯)
這本書位於機器學習與光子學的交界處。機器學習是計算機科學的一個子領域，專注於開發算法以解決形狀或圖像識別等挑戰性任務，而傳統算法在這些任務中往往無法奏效。光子學則是光的物理科學，支撐著我們信息社會中許多光學通信技術。本書提供了對儲存計算（reservoir computing）和現場可編程閘陣列（FPGAs）的全面介紹。
最近，儲存計算的光子實現（這是一種基於人工神經網絡的機器學習算法）使得光學計算的突破成為可能。在本書中，作者將這些系統的性能顯著推進到之前未曾達到的水平。通過將光子儲存計算機與高速電子設備（FPGA）進行接口，作者成功地實現了與儲存計算機的實時互動，這使他能夠大幅擴展其能力和潛在應用範圍。此外，作者利用他在機器學習和FPGA編程方面的專業知識，對一個截然不同的問題取得進展，即對動脈粥樣硬化的光學相干斷層掃描進行實時圖像分析。
