Controllable reset behavior in domain wall-magnetic tunnel junction artificial neurons for task-adaptable computation
This work provides methods for adapting spintronic artificial neurons to specific datasets, which is significant for researchers developing neuromorphic computing hardware.
This paper explores the implementation of edgy-relaxed behavior in domain wall-magnetic tunnel junction (DW-MTJ) artificial neurons using shape anisotropy, magnetic field, and current-driven soft reset mechanisms. The study demonstrates that this behavior improves classification accuracy and rate for ordered datasets, specifically the Optdigits handwritten digit dataset, while maintaining performance on randomized datasets.
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.