VCSEL-based photonic spiking neural networks for ultrafast detection and tracking

Tools

Robertson, Joshua and Kirkland, Paul and Di Caterina, Gaetano and Hurtado, Antonio (2024) VCSEL-based photonic spiking neural networks for ultrafast detection and tracking. Neuromorphic Computing and Engineering, 4 (1). 014010. ISSN 2634-4386 (https://doi.org/10.1088/2634-4386/ad2d5c)

[thumbnail of Robertson-etal-NCE-2024-VCSEL-based-photonic-spiking-neural-networks]
Preview
 Text. Filename: Robertson-etal-NCE-2024-VCSEL-based-photonic-spiking-neural-networks.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo
Download (1MB)| Preview

Abstract

Inspired by efficient biological spike-based neural networks, we demonstrate for the first time the detection and tracking of target patterns in image and video inputs at high-speed rates with networks of multiple artificial spiking optical neurons. Using photonic systems of in-parallel spiking vertical cavity surface emitting lasers (VCSELs), we demonstrate the implementation of multiple convolutional kernel operators which, in combination with optical spike signalling, enable the detection and tracking of target features in images/video feeds at an ultrafast photonic operation speed of 1 ns per pixel. Alongside a single layer optical spiking neural network (SNN) demonstration, a multi-layer network of photonic (GHz-rate) spike-firing neurons is reported where the photonic system successfully tracks a large complex feature (Handwritten Digit 3). The consecutive photonic layers perform spike-enabled image reduction and convolution operations, and interact with a software-implemented SNN, that learns the feature patterns that best identify the target to provide a high detection efficiency even in the presence of a distractor feature. This work therefore highlights the effectiveness of combining neuromorphic photonic hardware and software SNNs, for efficient learning and ultrafast operation, thanks to the use of spiking light signals, towards tackling complex AI and computer vision problems.

ORCID iDs

Robertson, Joshua, Kirkland, Paul ORCID logoORCID: https://orcid.org/0000-0001-5905-6816, Di Caterina, Gaetano ORCID logoORCID: https://orcid.org/0000-0002-7256-0897 and Hurtado, Antonio ORCID logoORCID: https://orcid.org/0000-0002-4448-9034;
CORE (COnnecting REpositories)