Abstract
We report both experimentally and in theory on the detection of edge features in digital images with an artificial optical spiking neuron based on a vertical-cavity surface-emitting laser (VCSEL). The latter delivers fast (< 100 ps) neuron-like optical spikes in response to optical inputs pre-processed using convolution techniques; hence representing image feature information with a spiking data output directly in the optical domain. The proposed technique is able to detect target edges of different directionalities in digital images by applying individual kernel operators and can achieve complete image edge detection using gradient magnitude. Importantly, the neuromorphic (brain-like) spiking edge detection of this work uses commercially sourced VCSELs exhibiting responses at sub-nanosecond rates (many orders of magnitude faster than biological neurons) and operating at the important telecom wavelength of 1300 nm; hence making our approach compatible with optical communication and data-centre technologies.
Highlights
Neuromorphic spiking models, developed to emulate the spiking responses of biological neurons in the brain [1], are seeing a rise in interest as new routes to novel computing paradigms are being explored
The push for photonic realisations has inspired a wave of optical spiking neuronal models based on numerous technologies such as phase change materials (PCMs) [6,7], resonant-tunnelling diodes (RTDs) [8,9], photonic cavities [10], optical modulators [11] and semiconductor lasers (SLs) [12,13,14,15,16,17,18,19,20,21,22,23,24,25]
The experimental setup used to implement the photonic vertical-cavity surface-emitting laser (VCSEL)-neuron and perform spiking image edge detection is shown in the schematic diagram of Fig. 1(b)
Summary
Neuromorphic spiking models, developed to emulate the spiking responses of biological neurons in the brain [1], are seeing a rise in interest as new routes to novel computing paradigms are being explored. Among the most promising SL neuronal models are vertical-cavity surface-emitting lasers (VCSELs) [18,19,20,21,22,23,24,25] These devices are reported to generate controllable excitable (spiking) dynamics along with key neuronal functionalities such as input thresholding and tonic spike activation [18,19]. Unlike reported implementations of CNNs, our technique utilizes time-division multiplexed optical inputs to enable operation with a single VCSEL-neuron; reducing hardware requirements, and provides output data in a spiking representation, directly in the optical domain We provide both experimental findings and theoretical results modelling the anticipated spiking responses of the VCSEL-neuron based on the SFM [26,27].
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