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|Title:||Non-Volatile In-Memory Computing by Spintronics||Authors:||Yu, Hao
|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
|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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