Abstract
In this work, we present numerical results concerning a multilayer “deep” photonic spiking convolutional neural network, arranged so as to tackle a 2D image classification task. The spiking neurons used are typical two-section quantum-well vertical-cavity surface-emitting lasers that exhibit isomorphic behavior to biological neurons, such as integrate-and-fire excitability and timing encoding. The isomorphism of the proposed scheme to biological networks is extended by replicating the retina ganglion cell for contrast detection in the photonic domain and by utilizing unsupervised spike dependent plasticity as the main training technique. Finally, in this work we also investigate the possibility of exploiting the fast carrier dynamics of lasers so as to time-multiplex spatial information and reduce the number of physical neurons used in the convolutional layers by orders of magnitude. This last feature unlocks new possibilities, where neuron count and processing speed can be interchanged so as to meet the constraints of different applications.
Highlights
Recent technological advances in terms of hardware and software over the last few decades have unleashed the computational capabilities of modern processors, so as to tackle stringent problems with unparalleled efficiency
A multitude of photonic spiking neurons have been studied both theoretically and experimentally, such as: two-section gain-absorber lasers [13], microring and disk lasers [14,15,16], single section quantum dot lasers [17,18], nanocavities based on 2D photonic crystals [19,20], optically injected lasers [21,22], lasers subjected to optical feedback [23,24] and vertical cavity surface emitting lasers (VCSELs) [9,25,26,27,28,29,30,31,32,33]
We describe in detail the model used to simulate the VCSEL’s dynamics [38] with all its mathematical equations and parameters
Summary
Recent technological advances in terms of hardware and software over the last few decades have unleashed the computational capabilities of modern processors, so as to tackle stringent problems with unparalleled efficiency. By mimicking the brain’s framework and function, spiking neural networks encode incoming analogue data to a sparse train of spikes, where information resides in the temporal domain These features result in a significant reduction of energy consumption while at the same time rendering the computational scheme resilient to noise [6,7,8]. Summarizing, this work provides the first, according to our knowledge, investigation of a full-scale PSCNN that simultaneously merges approaches such as: multiple convolutional layers for feature extraction, unsupervised STDP as training technique, time multiplexing of incoming signals so as to reduce neuron count and retina-ganglion-cell based structures so as to replace costly digital processing with a bioinspired process. A detailed comparison between our work and other VCSEL networks is presented
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