Photonic perceptron based on a Kerr microcomb for high-speed, scalable,
optical neural networks
Abstract
Optical artificial neural networks (ONNs) — analog computing
hardware tailored for machine learning — have significant potential
for ultra-high computing speed and energy efficiency. We propose a new
approach to architectures for ONNs based on integrated Kerr micro-comb
sources that is programmable, highly scalable and capable of reaching
ultra-high speeds. We experimentally demonstrate the building block of
the ONN — a single neuron perceptron — by mapping synapses onto 49
wavelengths of a micro-comb to achieve a high single-unit throughput of
11.9 Giga-FLOPS at 8 bits per FLOP, corresponding to 95.2 Gbps. We test
the perceptron on simple standard benchmark datasets —
handwritten-digit recognition and cancer-cell detection — achieving
over 90% and 85% accuracy, respectively. This performance is a direct
result of the record small wavelength spacing (49GHz) for a coherent
integrated microcomb source, which results in an unprecedented number of
wavelengths for neuromorphic optics. Finally, we propose an approach to
scaling the perceptron to a deep learning network using the same single
micro-comb device and standard off-the-shelf telecommunications
technology, for high-throughput operation involving full matrix
multiplication for applications such as real-time massive data
processing for unmanned vehicle and aircraft tracking.
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