Toward Fast Neural Computing using All-Photonic Phase Change Spiking Neurons

Abstract

The rapid growth of brain-inspired computing coupled with the inefficiencies in the CMOS implementations of neuromrphic systems has led to intense exploration of efficient hardware implementations of the functional units of the brain, namely, neurons and synapses. However, efforts have largely been invested in implementations in the electrical domain with potential limitations of switching speed, packing density of large integrated systems and interconnect losses. As an alternative, neuromorphic engineering in the photonic domain has recently gained attention. In this work, we demonstrate a purely photonic operation of an Integrate-and-Fire Spiking neuron, based on the phase change dynamics of Ge${}_2$Sb${}_2$Te${}_5$ (GST) embedded on top of a microring resonator, which alleviates the energy constraints of PCMs in electrical domain. We also show that such a neuron can be potentially integrated with on-chip synapses into an all-Photonic Spiking Neural network inferencing framework which promises to be ultrafast and can potentially offer a large operating bandwidth.

arXiv comment10 pages, 5 figures, accepted in Nature Scientific Reports

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• Analysis of Diffractive Optical Neural Networks and Their Integration with Electronic Neural Networks
• Prospects and applications of photonic neural networks
• Class-specific Differential Detection in Diffractive Optical Neural Networks Improves Inference Accuracy
• A Photonic In-Memory Computing primitive for Spiking Neural Networks using Phase-Change Materials
• Modeling Electrical Switching of Nonvolatile Phase-Change Integrated Nanophotonic Structures with Graphene Heaters
• Photonic spiking neural networks and CMOS-compatible graphene-on-silicon spiking neurons
• All-Optical Spiking Neuron Based On Passive Micro-Resonator
• Photonic neural networks based on integrated silicon microresonators
• Band-to-Band Tunneling based Ultra-Energy Efficient Silicon Neuron
• Optoelectronic Intelligence
• Emergent self-adaptation in an integrated photonic neural network for backpropagation-free learning
• Optically-triggered deterministic spiking regimes in nanostructure resonant tunnelling diode-photodetectors
• Contemporary implementations of spiking bio-inspired neural networks