Photonic Perceptron Based on a Kerr Microcomb for High-Speed, Scalable, Optical Neural Networks

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

1 Scopus Citations
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Author(s)

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

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number2000070
Journal / PublicationLaser and Photonics Reviews
Volume14
Issue number10
Online published6 Aug 2020
Publication statusPublished - Oct 2020

Abstract

Optical artificial neural networks (ONNs)—analog computing hardware tailored for machine learning—have significant potential for achieving ultra-high computing speed and energy efficiency. A new approach to architectures for ONNs based on integrated Kerr microcomb sources that is programmable, highly scalable, and capable of reaching ultra-high speeds is proposed here. The building block of the ONN—a single neuron perceptron—is experimentally demonstrated that reaches a high single-unit throughput speed of 11.9 Giga-FLOPS at 8 bits per FLOP, corresponding to 95.2 Gbps, achieved by mapping synapses onto 49 wavelengths of a microcomb. The perceptron is tested 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 low wavelength spacing (49 GHz) for a coherent integrated microcomb source, which results in an unprecedented number of wavelengths for neuromorphic optics. Finally, an approach to scaling the perceptron to a deep learning network is proposed using the same single microcomb 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 vehicles and aircraft tracking.

Research Area(s)

  • Kerr micro-comb, machine learning, optical neural networks, photonic perceptron

Citation Format(s)

Photonic Perceptron Based on a Kerr Microcomb for High-Speed, Scalable, Optical Neural Networks. / Xu, Xingyuan; Tan, Mengxi; Corcoran, Bill; Wu, Jiayang; Nguyen, Thach G.; Boes, Andreas; Chu, Sai T.; Little, Brent E.; Morandotti, Roberto; Mitchell, Arnan; Hicks, Damien G.; Moss, David J.

In: Laser and Photonics Reviews, Vol. 14, No. 10, 2000070, 10.2020.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal