  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.
