Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/85458
Title: Non-Volatile In-Memory Computing by Spintronics
Authors: Yu, Hao
Ni, Leibin
Wang, Yuhao
Keywords: In-memory computing
Spintronics
Issue Date: 2017
Publisher: Morgan & Claypool
Source: Yu, H., Ni, L., & Wang, Y. (2016). Non-Volatile In-Memory Computing by Spintronics. In K. Iniewski (Ed.), Synthesis Lectures on Emerging Engineering Technologies (Lecture #8). San Rafael, California: Morgan & Claypool.
Series/Report no.: Synthesis Lectures on Emerging Engineering Technologies
Abstract: 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.
URI: https://hdl.handle.net/10356/85458
http://hdl.handle.net/10220/43703
ISBN: 9781627052948
ISSN: 2381-1412
DOI: 10.2200/S00736ED1V01Y201609EET008
Rights: © 2017 Morgan & Claypool
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Books & Book Chapters

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