Working Paper Article Version 1 This version is not peer-reviewed

High-Speed Optical Neural Networks Based on Microcombs

Version 1 : Received: 11 March 2020 / Approved: 12 March 2020 / Online: 12 March 2020 (03:41:39 CET)

How to cite: Xu, X.; Tan, M.; Corcoran, B.; Wu, J.; Nguyen, T.G.; Boes, A.; Chu, S.T.; Little, B.E.; Morandotti, R.; Mitchell, A.; Hicks, D.G.; Moss, D.J. High-Speed Optical Neural Networks Based on Microcombs. Preprints 2020, 2020030193 Xu, X.; Tan, M.; Corcoran, B.; Wu, J.; Nguyen, T.G.; Boes, A.; Chu, S.T.; Little, B.E.; Morandotti, R.; Mitchell, A.; Hicks, D.G.; Moss, D.J. High-Speed Optical Neural Networks Based on Microcombs. Preprints 2020, 2020030193

Abstract

Optical artificial neural networks (ONNs) — analog computing hardware tailored for machine learning [1,2] — have significant potential for ultra-high computing speed and energy efficiency [3]. We propose a new approach to architectures for ONNs based on integrated Kerr micro-comb sources [4] 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.

Subject Areas

optical neural network; microcomb

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