Benchmarking Inverse Rashba-Edelstein Magnetoelectric Devices for Neuromorphic Computing
Andrew W. Stephan, Jiaxi Hu, and Steven J. Koester
TL;DR
This paper introduces a novel spintronic neural network design utilizing magnetoelectric and inverse Rashba-Edelstein effects, demonstrating improved speed and efficiency through simulation analysis.
Contribution
It presents a new cellular neural network architecture with spintronic neurons and CMOS synapses leveraging magnetoelectric and inverse Rashba-Edelstein effects.
Findings
Enhanced speed and efficiency over existing spintronic neural networks
Successful simulation-based performance validation
Innovative integration of effects for neural emulation
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.
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Taxonomy
TopicsAdvanced Memory and Neural Computing · Ferroelectric and Negative Capacitance Devices · Neural Networks and Applications