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arXiv · 2101.03095

Controllable reset behavior in domain wall-magnetic tunnel junction artificial neurons for task-adaptable computation

Samuel Liu·Christopher H. Bennett·Joseph S. Friedman·Matthew J. Marinella·David Paydarfar·Jean Anne C. Incorvia
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Abstract

Neuromorphic computing with spintronic devices has been of interest due to the limitations of CMOS-driven von Neumann computing. Domain wall-magnetic tunnel junction (DW-MTJ) devices have been shown to be able to intrinsically capture biological neuron behavior. Edgy-relaxed behavior, where a frequently firing neuron experiences a lower action potential threshold, may provide additional artificial neuronal functionality when executing repeated tasks. In this study, we demonstrate that this behavior can be implemented in DW-MTJ artificial neurons via three alternative mechanisms: shape anisotropy, magnetic field, and current-driven soft reset. Using micromagnetics and analytical device modeling to classify the Optdigits handwritten digit dataset, we show that edgy-relaxed behavior improves both classification accuracy and classification rate for ordered datasets while sacrificing little to no accuracy for a randomized dataset. This work establishes methods by which artificial spintronic neurons can be flexibly adapted to datasets.

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Samuel Liu, Christopher H. Bennett, Joseph S. Friedman, Matthew J. Marinella, David Paydarfar, Jean Anne C. Incorvia. 2021-01-08. Controllable reset behavior in domain wall-magnetic tunnel junction artificial neurons for task-adaptable computation. https://doi.org/10.1109/lmag.2021.3069666

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