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Non-Volatile In-Memory Computing by Spintronics [electronic resource] / by Hao Yu, Leibin Ni, Yuhao Wang.

By: Yu, Hao [author.].
Contributor(s): Ni, Leibin [author.] | Wang, Yuhao [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Synthesis Lectures on Emerging Engineering Technologies: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2017Edition: 1st ed. 2017.Description: XIII, 147 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783031020322.Subject(s): Engineering | Electrical engineering | Electronic circuits | Computers | Materials science | Surfaces (Technology) | Thin films | Technology and Engineering | Electrical and Electronic Engineering | Electronic Circuits and Systems | Computer Hardware | Materials Science | Surfaces, Interfaces and Thin FilmAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 620 Online resources: Click here to access online
Contents:
Preface -- Acknowledgments -- Introduction -- Non-volatile Spintronic Device and Circuit -- In-memory Data Encryption -- In-memory Data Analytics -- Authors' Biographies .
In: Springer Nature eBookSummary: 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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Preface -- Acknowledgments -- Introduction -- Non-volatile Spintronic Device and Circuit -- In-memory Data Encryption -- In-memory Data Analytics -- Authors' Biographies .

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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