@inproceedings{186674e42f8642f8a21d20e611d616b9,
title = "Self-Resetting Magnetic Tunnel Junction Neuron-based Spiking Neural Networks",
abstract = "Spintronic devices such as the magnetic tunnel junction show significant potential for energy-efficient neuromorphic computing applications. This paper presents a spintronic magnetic tunnel junction neuromorphic device capable of integration, spike, and self-reset neuron characteristics. The spin-orbit drives the neuron magnetization dynamics, which controls the neuron characteristics. The input pixels are encoded in the amplitude of the current, which controls the spiking frequency of the neuron. We model the neuron characteristics into a compact model to integrate the proposed spiking neuron into a 3-layer SNN and CSNN architecture. We train and test the spiking neuron model to classify the MNIST and FMNIST datasets. The network achieves classification accuracy above 97% on MNIST and 91% on FMNIST. Considering the classification performance, self-resetting functionality, and nanosecond operation range, the proposed device shows a substantial potential for energy-efficient neuromorphic computing.",
keywords = "and Neuromorphic Computing, Magnetic tunnel junction, Spiking neural networks (SNN), spiking neurons, Spintronics",
author = "Lone, {Aijaz H.} and Rahimi, {Daniel N.} and Hossein Fariborzi and Gianluca Setti",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 6th IEEE International Conference on AI Circuits and Systems, AICAS 2024 ; Conference date: 22-04-2024 Through 25-04-2024",
year = "2024",
doi = "10.1109/AICAS59952.2024.10595898",
language = "English (US)",
series = "2024 IEEE 6th International Conference on AI Circuits and Systems, AICAS 2024 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "119--123",
booktitle = "2024 IEEE 6th International Conference on AI Circuits and Systems, AICAS 2024 - Proceedings",
address = "United States",
}