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.
@article{arxiv.2101.03095,
title = {Controllable reset behavior in domain wall-magnetic tunnel junction artificial neurons for task-adaptable computation},
author = {Samuel Liu and Christopher H. Bennett and Joseph S. Friedman and Matthew J. Marinella and David Paydarfar and Jean Anne C. Incorvia},
journal= {arXiv preprint arXiv:2101.03095},
year = {2021}
}