@inproceedings{4eb494c2d45344518ca12317957e7377,
title = "Discrete Anomalous Hall Resistance-based Quantized Convolutional Neural Network",
abstract = "Spintronic devices have shown promise for energy-efficient storage and neuromorphic computing. In this abstract, we present the realization of a spintronic device exhibiting discrete anomalous Hall resistance states. We attribute this discrete resistance behavior to the magnetic domain wall pinning and depinning and gradual switching of different magnetic layers. The number of resistance states is a function of the temperature. Furthermore, this discrete resistance behavior of the device allows us to employ these resistance states as weights in a quantized convolutional neural network. The network is trained and tested on the CIFAR-10 data set and the system achieves an accuracy of around 86.95%.",
keywords = "Anomalous Hall effect, Discrete resistive states, Domain wall pinning, neuromorphic computing, Spintronics",
author = "Lone, {Aijaz H.} and Xuecui Zou and Hanrui Li and Nazek El-Atab and Hossein Fariborzi",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Magnetic Conference - Short Papers, INTERMAG Short Papers 2023 ; Conference date: 15-05-2023 Through 19-05-2023",
year = "2023",
doi = "10.1109/INTERMAGShortPapers58606.2023.10228622",
language = "English (US)",
series = "2023 IEEE International Magnetic Conference - Short Papers, INTERMAG Short Papers 2023 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2023 IEEE International Magnetic Conference - Short Papers, INTERMAG Short Papers 2023 - Proceedings",
address = "United States",
}