Ultrafast optical integration and pattern classification for neuromorphic photonics based on spiking VCSEL neurons

Robertson, Joshua and Hejda, Matěj and Bueno, Julián and Hurtado, Antonio (2020) Ultrafast optical integration and pattern classification for neuromorphic photonics based on spiking VCSEL neurons. Scientific Reports, 10 (1). 6098. ISSN 2045-2322 (https://doi.org/10.1038/s41598-020-62945-5)

[thumbnail of Robertson-etal-SR-2020-Ultrafast-optical-integration-and-pattern-classification-for-neuromorphic]
Preview
 Text. Filename: Robertson_etal_SR_2020_Ultrafast_optical_integration_and_pattern_classification_for_neuromorphic.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo
Download (2MB)| Preview

Abstract

In today’s data-driven world, the ability to process large data volumes is crucial. Key tasks, such as pattern recognition and image classification, are well suited for artificial neural networks (ANNs) inspired by the brain. Neuromorphic computing approaches aimed towards physical realizations of ANNs have been traditionally supported by micro-electronic platforms, but recently, photonic techniques for neuronal emulation have emerged given their unique properties (e.g. ultrafast operation, large bandwidths, low cross-talk). Yet, hardware-friendly systems of photonic spiking neurons able to perform processing tasks at high speeds and with continuous operation remain elusive. This work provides a first experimental report of Vertical-Cavity Surface-Emitting Laser-based spiking neurons demonstrating different functional processing tasks, including coincidence detection and pattern recognition, at ultrafast rates. Furthermore, our approach relies on simple hardware implementations using off-the-shelf components. These results therefore hold exciting prospects for novel, compact and high-speed neuromorphic photonic platforms for future computing and Artificial Intelligence systems.

CORE (COnnecting REpositories)