期刊
IEEE JOURNAL OF SELECTED TOPICS IN QUANTUM ELECTRONICS
卷 29, 期 2, 页码 -出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTQE.2022.3205716
关键词
Vertical cavity surface emitting lasers; Neurons; Photonics; Reservoirs; Optical imaging; Nonlinear optics; Task analysis; Vertical cavity surface emitting lasers; semiconductor lasers; artificial neural networks; neural network hardware; spiking neural network; photonic reservoir computing
This study introduces and experimentally demonstrates a new GHz-rate photonic spiking neural network (SNN) built with a single VCSEL neuron. The system effectively implements a photonic VCSEL-based spiking reservoir computer and successfully applies it to a complex nonlinear classification task. With highly hardware-friendly and inexpensive realization, the proposed system shows great potential for future neuromorphic photonic spike-based processing systems.
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.
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