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

TL;DR

This paper demonstrates how domain wall-magnetic tunnel junction artificial neurons can be controlled to exhibit edgy-relaxed behavior, enhancing task-specific classification performance in neuromorphic computing.

Contribution

It introduces three mechanisms to implement edgy-relaxed behavior in DW-MTJ neurons, enabling flexible adaptation to different datasets.

Findings

Improved classification accuracy on ordered datasets

Enhanced classification rate with edgy-relaxed behavior

Minimal accuracy loss on randomized datasets

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.