Leveraging Intrinsic Physics of Spintronic Devices for Neuromorphic Computing

Abstract

Neuromorphic computing has made significant strides over the past few years by mapping functional primitives of neurons and synapses to nanoelectronic devices, yet most implementations do not tap into the temporal non-linear dynamics of the devices. We argue that spintronic devices, specifically magnetic tunnel junctions (MTJs), offer a palette of intrinsic physical phenomena that can serve as native computational primitives. Spin-transfer torque (STT), spin-orbit torque (SOT), voltage-controlled magnetic anisotropy (VCMA), magnetoelectric effect (ME) and stochastic superparamagnetic dynamics each enable functionalities difficult to replicate in deterministic CMOS. We underscore this perspective through two specific systemlevel instances focusing on the synapse and neuron: selectorless synaptic crossbar arrays that exploit a VCMA-driven switchingmechanism to eliminate per-cell selectors, and temporal information encoding through superparamagnetic neuronal MTJs that achieves significant network-level spiking sparsity via stochastic dynamics. Building on these exemplars, we envision how these primitives compose with the broader spintronic ecosystem — domain-wall synapses, oscillatory primitives, and stochastic neurons — toward neuromorphic systems that are simultaneously sparser, denser, and more functionally diverse.