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About this book
Exa-scale computing needs to re-examine the existing hardware platform that can support intensive data-oriented computing. Since the main bottleneck is from memory, we aim to develop an energy-efficient in-memory computing platform in this book. First, the models of spin-transfer torque magnetic tunnel junction and racetrack memory are presented. Next, we show that the spintronics could be a candidate for future data-oriented computing for storage, logic, and interconnect. As a result, by utilizing spintronics, in-memory-based computing has been applied for data encryption and machine learning. The implementations of in-memory AES, Simon cipher, as well as interconnect are explained in details. In addition, in-memory-based machine learning and face recognition are also illustrated in this book.
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Table of contents (4 chapters)
Authors and Affiliations
Bibliographic Information
Book Title: Non-Volatile In-Memory Computing by Spintronics
Authors: Hao Yu, Leibin Ni, Yuhao Wang
Series Title: Synthesis Lectures on Emerging Engineering Technologies
DOI: https://doi.org/10.1007/978-3-031-02032-2
Publisher: Springer Cham
eBook Packages: Synthesis Collection of Technology (R0), eBColl Synthesis Collection 7
Copyright Information: Springer Nature Switzerland AG 2017
Softcover ISBN: 978-3-031-00904-4Published: 02 December 2016
eBook ISBN: 978-3-031-02032-2Published: 31 May 2022
Series ISSN: 2381-1412
Series E-ISSN: 2381-1439
Edition Number: 1
Number of Pages: XIII, 147
Topics: Engineering, general, Electrical Engineering, Circuits and Systems, Computer Hardware, Materials Science, general, Surfaces and Interfaces, Thin Films