GHz Rate Neuromorphic Photonic Spiking Neural Network With a Single Vertical-Cavity Surface-Emitting Laser (VCSEL)

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, compactness, and ease of manufacturing. Of particular interest is the ability of VCSELs to exhibit neurallike spiking responses, much like biological neurons, but at ultrafast sub-nanosecond rates; thus offering great prospects for high-speed light-enabled neuromorphic (spike-based) processors. Recent works have shown the use the spiking dynamics in VC-SELs for pattern recognition and image processing problems such as image data encoding and edge-feature detection. Additionally, VCSELs have also been used recently as nonlinear elements in photonic reservoir computing (RC) implementations, yielding excellent state of the art operation. This work introduces and experimentally demonstrates for the first time the new concept of a 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 (built with a single VCSEL 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 spikebased information processing systems based upon VCSELs (or other laser types) for novel ultrafast machine learning and AI hardware.

Phasor-Based Analysis of a Neuromorphic Perceptron Architecture for Microwave Sensing

“…This output matches the output of the trained perceptrons when t = nT . By sampling the output signals every 5 ns, the perceptron output When comparing this work to similar works in [23,24], it becomes clear that there are distinct differences in approaches and performances. The hybrid coupler network relies on passive components such as directional couplers, microwave filters, and phase shifters for computation.…”

Brain-inspired nanophotonic spike computing: challenges and prospects

“…Optics and optoelectronics are among the key prospective technologies that will allow to overcome both interconnect energy and bandwidth density constraints of neuromorphic electronic approaches. Using photonics, light-speed neuron-like spikes can be achieved in semiconductor lasers namely using commercially available VCSELs [48,49], and other optoelectronic platforms (review in [50]). To decrease spatial footprint, approaches for optical neural networks are being tested using either mature III-V/Si photonic integrated platforms or fibre optics-based reservoir computing, therefore allowing for the implementation of coherent light-based deep learning, reservoir computing, and photonic accelerators (tensor cores) (review in [7]).…”

Spiking photonic reservoir computing system based on photonic spiking neuron

“…Therefore, the number of virtual nodes is limited with regard to the sampling frequency. Furthermore, the extreme learning machine (ELM) also belongs to the concept of reservoir computing and does not require time delay feedback [10] .Using this approach to build a single-node photonic reservoir can eliminate the feedback loop and reduce network complexity, and also allow for setting the number of virtual nodes flexibly through multiplying by different mask signals [11] . Besides, the photonic spiking neuron, which has high nonlinearity and represents information by temporal firing spikes has the advantage of low power consumption [12] and strong noise resistance [13] .…”