Non-Volatile In-Memory Computing by Spintronics

• Main Authors: Yu, Hao, Ni, Leibin, Wang, Yuhao
• Other Authors: Iniewski, Kris
• Format: Book
• Language: English
• Published: Morgan & Claypool 2017
• Subjects: In-memory computing; Spintronics
• Online Access: https://hdl.handle.net/10356/85458; http://hdl.handle.net/10220/43703
• Institution: Nanyang Technological University

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.