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arXiv 2003.01347physics.opticscs.ET
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Single photonic perceptron based on a soliton crystal Kerr microcomb for high-speed, scalable, optical neural networks

Xingyuan Xu, Mengxi Tan, Bill Corcoran, Jiayang Wu, Thach G. Nguyen, Andreas Boes, Sai T. Chu, Brent E. Little, Roberto Morandotti, Arnan Mitchell, Damien G. Hicks, David J. Moss

英文摘要:

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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