GHz rate neuromorphic photonic spiking neural network with a single Vertical-Cavity Surface-Emitting Laser (VCSEL)

Owen-Newns, Dafydd and Robertson, Joshua and Hejda, Matĕj and Hurtado, Antonio (2023) GHz rate neuromorphic photonic spiking neural network with a single Vertical-Cavity Surface-Emitting Laser (VCSEL). IEEE Journal of Selected Topics in Quantum Electronics, 29 (2). pp. 1-10. 1500110. ISSN 1077-260X (https://doi.org/10.1109/JSTQE.2022.3205716)

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Abstract

Vertical-Cavity Surface-Emitting Lasers (VCSELs) are highly promising devices for the construction of neuromorphic photonic information processing systems, due to their numerous desirable properties such as low power consumption, high modulation speed, and compactness. Of particular interest is the ability of VCSELs to exhibit neuron-like spiking responses at ultrafast sub-nanosecond rates; thus offering great prospects for high-speed light-enabled spike-based processors. Recent works have shown spiking VCSELs are capable of tackling pattern recognition and image processing problems, but additionally, VCSELs have been used as nonlinear elements in photonic reservoir computing (RC) implementations, yielding state of the art operation. This work introduces and experimentally demonstrates for the first time a new GHz-rate photonic spiking neural network (SNN) built with a single VCSEL neuron. The reported system effectively implements a photonic VCSEL-based spiking reservoir computer, and demonstrates its successful application to a complex nonlinear classification task. Importantly, the proposed system benefits from a highly hardware-friendly, inexpensive realization (a single VCSEL device and off-the-shelf fibre-optic components), for high-speed (GHz-rate inputs) and low-power (sub-mW optical input power) photonic operation. These results open new pathways towards future neuromorphic photonic spike-based processing systems based upon VCSELs (or other laser types) for novel ultrafast machine learning and AI hardware.

Persistent Identifier

https://doi.org/10.17868/strath.00082286
  • Item type:Article
    ID code:82286
    Dates:
    Date
    Event
    March 2023
    Published
    12 September 2022
    Published Online
    6 September 2022
    Accepted
    15 June 2022
    Submitted
    Notes:Published in: IEEE Journal of Selected Topics in Quantum Electronics ( Volume: 29, Issue: 2: Optical Computing, March-April 2023)
    Subjects:Science > Physics
    Department:Faculty of Science > Physics
    Faculty of Science > Physics > Institute of Photonics
    Depositing user: Pure Administrator
    Date deposited:09 Sep 2022 11:32
    Last modified:09 Apr 2024 03:19
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/82286
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