Application of FPGA to Real‐Time Machine Learning: Hardware Reservoir Computers and Software Image Processing (Springer Theses)
Piotr Antonik
- 出版商: Springer
- 出版日期: 2018-05-31
- 售價: $4,780
- 貴賓價: 9.5 折 $4,541
- 語言: 英文
- 頁數: 171
- 裝訂: Hardcover
- ISBN: 3319910523
- ISBN-13: 9783319910529
-
相關分類:
FPGA、Machine Learning
海外代購書籍(需單獨結帳)
買這商品的人也買了...
-
$399The Design Warrior's Guide to FPGAs
-
$1,000$980 -
$399Satellite Networking: Principles and Protocols (Hardcover)
-
$969Rapid System Prototyping with FPGAs: Accelerating the Design Process (Paperback)
-
$2,140$2,033 -
$1,450$1,421 -
$1,940$1,843 -
$1,040$988 -
$2,000$1,900 -
$340$323 -
$1,760$1,672 -
$301Xilinx Zynq SoC 與嵌入式 Linux 設計實戰指南 (兼容ARM Cortex-A9的設計方法)
-
$1,310$1,245 -
$590$561 -
$650$553 -
$860$731 -
$4,133FPGA-based Implementation of Signal Processing Systems, 2/e (Hardcover)
-
$352Verilog HDL數字系統設計及模擬(第2版)
-
$390$371 -
$296SC-CFDMA 無線傳輸技術
-
$1,560$1,529 -
$534$507 -
$758AI 芯片:前沿技術與創新未來
-
$8575G 與衛星通信融合之道:標準化與創新
-
$599$569
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.