Developing All-Skyrmion Spiking Neural Network
This work addresses energy efficiency in neuromorphic computing for applications like pattern recognition, though it appears incremental as it builds on existing skyrmion and SNN concepts with a novel integration.
The paper tackles the problem of implementing neuromorphic computing by proposing an All-Skyrmion Spiking Neural Network (AS-SNN) that uses skyrmions for spike signal transmission, achieving 87.1% inference accuracy on handwritten digit recognition with energy dissipation of ~1 fJ per spike, which is three orders of magnitude lower than CMOS-based systems.
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.