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

• URL: http://arxiv.org/abs/2101.03095v1
• Date: Fri, 8 Jan 2021 16:50:29 GMT
• Title: Controllable reset behavior in domain wall-magnetic tunnel junction artificial neurons for task-adaptable computation
• Authors: Samuel Liu, Christopher H. Bennett, Joseph S. Friedman, Matthew J. Marinella, David Paydarfar, Jean Anne C. Incorvia
• Abstract summary: Domain wall-magnetic tunnel junction (DW-MTJ) devices have been shown to be able to intrinsically capture biological neuron behavior. We show that edgy-relaxed behavior can be implemented in DW-MTJ artificial neurons via three alternative mechanisms.
• Score: 1.4505273244528207
• License: http://creativecommons.org/licenses/by/4.0/
• 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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