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