  Magnetic skyrmions have attracted considerable interest, especially after
their recent experimental demonstration at room temperature in multilayers. The
robustness, nanoscale size and non-volatility of skyrmions have triggered a
substantial amount of research on skyrmion-based low-power, ultra-dense
nanocomputing and neuromorphic systems such as artificial synapses.
Room-temperature operation is required to integrate skyrmionic synapses in
practical future devices. Here, we numerically propose a nanoscale skyrmionic
synapse composed of magnetic multilayers that enables room-temperature device
operation tailored for optimal synaptic resolution. We demonstrate that when
embedding such multilayer skyrmionic synapses in a simple spiking neural
network (SNN) with unsupervised learning via the spike-timing-dependent
plasticity rule, we can achieve only a 78% classification accuracy in the MNIST
handwritten data set under realistic conditions. We propose that this
performance can be significantly improved to about 98.61% by using a deep SNN
with supervised learning. Our results illustrate that the proposed skyrmionic
synapse can be a potential candidate for future energy-efficient neuromorphic
edge computing.
