Prospects Of Efficient Neural Computing With Arrays Of Magneto-Metallic Neurons And Synapses

Non-von Neumann computing models, like Artificial and Spiking Neural Networks, inspired from the functionalities of the human brain, would require devices that can offer a direct mapping to the underlying neuroscience mechanisms for energy-efficient and compact hardware implementation. To that effect, spin-transfer torque phenomena in devices based on lateral spin valves, domain wall motion in magnets and magnetic tunnel junctions can potentially pave the way for spintronic neural computing systems, where spintronic neurons interfaced with spintronic synapses, can directly mimic biological neural and synaptic functionalities. We explore various device structures suitable for such non-Boolean functionalities and demonstrate the potential benefits of such neural computing based on arrays of magneto-metallic neurons and synapses.