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Nanomagnets driven by spin currents provide a natural implementation for aneuron and a synapse: currents allow convenient summation of multiple inputs,while the magnet provides the threshold function. The objective of this paperis to explore the possibility of a hardware neural network (HNN) implementationusing a spin switch (SS) as its basic building block. SS is a recently proposeddevice based on established technology with a transistor-like gain andinput-output isolation. This allows neural networks to be constructed withpurely passive interconnections without intervening clocks or amplifiers. Theweights for the neural network are conveniently adjusted through analogvoltages that can be stored in a non-volatile manner in an underlying CMOSlayer using a floating gate low dropout voltage regulator. The operation of amulti-layer SS neural network designed for character recognition isdemonstrated using a standard simulation model based on coupledLandau-Lifshitz-Gilbert (LLG) equations, one for each magnet in the network.

Spin switches for compact implementation of neuron and synapse