A Nanoscale Room-Temperature Multilayer Skyrmionic Synapse for Deep Spiking Neural Networks
| Autor: | Steve Furber, Giacomo Indiveri, Chen Li, Christoforos Moutafis, Vasilis F. Pavlidis, Runze Chen, J.J. Miles, Yu Li |
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| Rok vydání: | 2020 |
| Předmět: |
Computer science
General Physics and Astronomy FOS: Physical sciences 02 engineering and technology Applied Physics (physics.app-ph) Topology 01 natural sciences Robustness (computer science) 0103 physical sciences Mesoscale and Nanoscale Physics (cond-mat.mes-hall) 010306 general physics Edge computing Spiking neural network Condensed Matter - Materials Science Condensed Matter - Mesoscale and Nanoscale Physics Skyrmion Supervised learning Materials Science (cond-mat.mtrl-sci) Physics - Applied Physics Computational Physics (physics.comp-ph) 021001 nanoscience & nanotechnology Neuromorphic engineering Unsupervised learning Embedding 0210 nano-technology Physics - Computational Physics |
| DOI: | 10.48550/arxiv.2009.14462 |
| Popis: | 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. Comment: 14 pages, 10 figures |
| Databáze: | OpenAIRE |
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