  Over the past decade Spiking Neural Networks (SNN) have emerged as one of the
popular architectures to emulate the brain. In SNN, information is temporally
encoded and communication between neurons is accomplished by means of spikes.
In such networks, spike-timing dependent plasticity mechanisms require the
online programming of synapses based on the temporal information of spikes
transmitted by spiking neurons. In this work, we propose a spintronic synapse
with decoupled spike transmission and programming current paths. The spintronic
synapse consists of a ferromagnet-heavy metal heterostructure where programming
current through the heavy metal generates spin-orbit torque to modulate the
device conductance. Low programming energy and fast programming times
demonstrate the efficacy of the proposed device as a nanoelectronic synapse. We
perform a simulation study based on an experimentally benchmarked
device-simulation framework to demonstrate the interfacing of such spintronic
synapses with CMOS neurons and learning circuits operating in transistor
sub-threshold region to form a network of spiking neurons that can be utilized
for pattern recognition problems.
