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Non-Volatile In-Memory Computing by Spintronics / by Hao Yu, Leibin Ni, Yuhao Wang

By: Yu, Hao, (Electrical engineer), autor
Contributor(s): Ni, Leibin, autor | Wang, Yuhao, (Electrical engineer), autor
Material type: materialTypeLabelE-bookSeries: (Synthesis Lectures on Emerging Engineering Technologies, 2381-1439).Publisher: Cham : Springer International Publishing, 2017Edition: 1st edition 2017.Description: 1 recurso en línea (XIII, 147 páginas).ISBN: 9783031020322.Subject: Espintrónica | Dispositivos de almacenamiento de datosOnline resources: Acceso a este recurso digital (usuarios Universidad Europea de Madrid)Digital Resources
Contents:
Preface -- Acknowledgments -- Introduction -- Non-volatile Spintronic Device and Circuit -- In-memory Data Encryption -- In-memory Data Analytics -- Authors' Biographies .
Summary: 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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Holdings
Item type Current library Collection Call number Status Date due Barcode Item holds
LIBRO-E NO PRÉSTAMO LIBRO-E NO PRÉSTAMO Madrid Digital Acceso Electrónico (UEM) Ciencias e Ingeniería TK7874.887 2017 EB (Browse shelf(Opens below)) Acceso electrónico eBook.01112525
Total holds: 0

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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