  In this work, we have proposed a revolutionary neuromorphic computing
methodology to implement All-Skyrmion Spiking Neural Network (AS-SNN). Such
proposed methodology is based on our finding that skyrmion is a topological
stable spin texture and its spatiotemporal motion along the magnetic nano-track
intuitively interprets the pulse signal transmission between two interconnected
neurons. In such design, spike train in SNN could be encoded as particle-like
skyrmion train and further processed by the proposed skyrmion-synapse and
skyrmion-neuron within the same magnetic nano-track to generate output skyrmion
as post-spike. Then, both pre-neuron spikes and post-neuron spikes are encoded
as particle-like skyrmions without conversion between charge and spin signals,
which fundamentally differentiates our proposed design from other hybrid
Spin-CMOS designs. The system level simulation shows 87.1% inference accuracy
for handwritten digit recognition task, while the energy dissipation is ~1
fJ/per spike which is 3 orders smaller in comparison with CMOS based IBM
TrueNorth system.
