Please use this identifier to cite or link to this item: https://doi.org/10.1109/JXCDC.2022.3227774
Title: Self-Reset Schemes for Magnetic Domain Wall-Based Neuron
Authors: Das, Debasis 
Fong, Xuanyao 
Keywords: Science & Technology
Technology
Computer Science, Hardware & Architecture
Computer Science
Magnetic domains
magnetic domain wall (DW)
micromagnetics
neuromorphics
spintronics
MOTION
Issue Date: Dec-2022
Publisher: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation: Das, Debasis, Fong, Xuanyao (2022-12). Self-Reset Schemes for Magnetic Domain Wall-Based Neuron. IEEE JOURNAL ON EXPLORATORY SOLID-STATE COMPUTATIONAL DEVICES AND CIRCUITS 8 (2) : 166-172. ScholarBank@NUS Repository. https://doi.org/10.1109/JXCDC.2022.3227774
Abstract: Spintronic artificial spiking neurons are promising due to their ability to closely mimic the leaky integrate-and-fire (LIF) dynamics of the biological LIF spiking neuron. However, the neuron needs to be reset after firing. Few of the spintronic neurons that have been proposed in the literature discuss the reset process in detail. In this article, we discuss the various schemes to achieve this reset in a magnetic domain wall (DW)-based spintronic neuron in which the position of the DW represents the membrane potential. In all the spintronic neurons studied, the neuron enters a refractory period and is reset when the DW reaches a particular position. We show that the self-reset operation in the neuron devices consumes energy that can vary from several pJ to a few fJ, which highlights the importance of the reset strategy in improving the energy efficiency of spintronic artificial spiking neurons.
Source Title: IEEE JOURNAL ON EXPLORATORY SOLID-STATE COMPUTATIONAL DEVICES AND CIRCUITS
URI: https://scholarbank.nus.edu.sg/handle/10635/245748
ISSN: 2329-9231
DOI: 10.1109/JXCDC.2022.3227774
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