  Lateral inhibition is an important functionality in neuromorphic computing,
modeled after the biological neuron behavior that a firing neuron deactivates
its neighbors belonging to the same layer and prevents them from firing. In
most neuromorphic hardware platforms lateral inhibition is implemented by
external circuitry, thereby decreasing the energy efficiency and increasing the
area overhead of such systems. Recently, the domain wall -- magnetic tunnel
junction (DW-MTJ) artificial neuron is demonstrated in modeling to be
inherently inhibitory. Without peripheral circuitry, lateral inhibition in
DW-MTJ neurons results from magnetostatic interaction between neighboring
neuron cells. However, the lateral inhibition mechanism in DW-MTJ neurons has
not been studied thoroughly, leading to weak inhibition only in very
closely-spaced devices. This work approaches these problems by modeling
current- and field- driven DW motion in a pair of adjacent DW-MTJ neurons. We
maximize the magnitude of lateral inhibition by tuning the magnetic interaction
between the neurons. The results are explained by current-driven DW velocity
characteristics in response to external magnetic field and quantified by an
analytical model. Finally, the dependence of lateral inhibition strength on
device parameters is investigated. This provides a guideline for the
optimization of lateral inhibition implementation in DW-MTJ neurons. With
strong lateral inhibition achieved, a path towards competitive learning
algorithms such as the winner-take-all are made possible on such neuromorphic
devices.
