Performance Estimate of Inverse Rashba-Edelstein Magnetoelectric Devices for Neuromorphic Computing

Andrew W. Stephan; Jiaxi Hu; Steven J. Koester

doi:10.1109/JXCDC.2019.2903286

Performance Estimate of Inverse Rashba-Edelstein Magnetoelectric Devices for Neuromorphic Computing

Andrew W. Stephan, Jiaxi Hu, Steven J. Koester

Electrical and Computer Engineering

Research output: Contribution to journal › Article › peer-review

5 Scopus citations

Abstract

We propose a new design for a cellular neural network with spintronic neurons and CMOS-based synapses. Harnessing the magnetoelectric and inverse Rashba-Edelstein effects allows natural emulation of the behavior of an ideal cellular network. This combination of effects offers an increase in speed and efficiency over other spintronic neural networks. A rigorous performance analysis via simulation is provided.

Original language	English (US)
Article number	8661619
Pages (from-to)	25-33
Number of pages	9
Journal	IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Volume	5
Issue number	1
DOIs	https://doi.org/10.1109/JXCDC.2019.2903286
State	Published - Jun 2019

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Publisher Copyright:
© 2014 IEEE.

Keywords

CMOS
Cellular neural network (CNN)
Rashba-Edelstein
energy efficiency
magnetoelectric (ME)
spintronics

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10.1109/JXCDC.2019.2903286

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abstract = "We propose a new design for a cellular neural network with spintronic neurons and CMOS-based synapses. Harnessing the magnetoelectric and inverse Rashba-Edelstein effects allows natural emulation of the behavior of an ideal cellular network. This combination of effects offers an increase in speed and efficiency over other spintronic neural networks. A rigorous performance analysis via simulation is provided.",

keywords = "CMOS, Cellular neural network (CNN), Rashba-Edelstein, energy efficiency, magnetoelectric (ME), spintronics",

author = "Stephan, {Andrew W.} and Jiaxi Hu and Koester, {Steven J.}",

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AU - Stephan, Andrew W.

AU - Hu, Jiaxi

AU - Koester, Steven J.

PY - 2019/6

Y1 - 2019/6

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AB - We propose a new design for a cellular neural network with spintronic neurons and CMOS-based synapses. Harnessing the magnetoelectric and inverse Rashba-Edelstein effects allows natural emulation of the behavior of an ideal cellular network. This combination of effects offers an increase in speed and efficiency over other spintronic neural networks. A rigorous performance analysis via simulation is provided.

KW - CMOS

KW - Cellular neural network (CNN)

KW - Rashba-Edelstein

KW - energy efficiency

KW - magnetoelectric (ME)

KW - spintronics

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ER -

Performance Estimate of Inverse Rashba-Edelstein Magnetoelectric Devices for Neuromorphic Computing

Abstract

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