Spike-based information encoding in vertical cavity surface emitting lasers for neuromorphic photonic systems

Research output: Contribution to journalArticle

Abstract

The ongoing growth of use-cases for artificial neural networks (ANNs) fuels the search for new, tailor-made ANN-optimized hardware. Neuromorphic (brain-like) computers are among the proposed highly promising solutions, with optical neuromorphic realizations recently receiving increasing research interest. Among these, photonic neuronal models based on vertical cavity surface emitting lasers (VCSELs) stand out due to their favourable properties, fast operation and mature technology. In this work, we experimentally demonstrate different strategies to encode information into ultrafast spiking events in a VCSEL-neuron. We evaluate how the strength of the input perturbations (stimuli) influences the spike activation time, allowing for spike latency input coding. Based on a study of refractory behaviour in the system, we demonstrate the capability of the VCSEL-neuron to perform reliable binary-to-spike information coding with spiking rates surpassing 1 GHz. We also report experimentally on neuro-inspired spike firing rate-coding with a VCSEL-neuron, where the strength of the input perturbation (stimulus) is continuously encoded into the spiking frequency (spike firing rate). With the prospects of neuromorphic photonic systems constantly growing, we believe the reported functionalities with the ultrafast spiking VCSEL-neurons provide a reliable, multifaceted approach for interfacing photonic neuromorphic platforms with existing computation and communication systems.
Original languageEnglish
Article number044001
Number of pages10
JournalJournal of Physics: Photonics
Volume2
Issue number4
Early online date15 Jul 2020
DOIs
Publication statusPublished - 12 Aug 2020

Keywords

  • neuromorphic photonics
  • vertical cavity surface emitting laser
  • optical neuron
  • spiking laser neuron
  • spike encoding

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