Single perceptron at 12 GigaOPs based on a microcomb for versatile, high-speed scalable, optical neural networks

M. Tan; X. Xu; J. Wu; A. Boes; B. Corcoran; T. G. Nguyen; S. T. Chu; B. E. Little; R. Morandotti; A. Mitchell; D. G. Hicks; D. J. Moss

doi:10.1117/12.2584011

5 March 2021 Single perceptron at 12 GigaOPs based on a microcomb for versatile, high-speed scalable, optical neural networks

M. Tan, X. Xu, J. Wu, A. Boes, B. Corcoran, T. G. Nguyen, S. T. Chu, B. E. Little, R. Morandotti, A. Mitchell, D. G. Hicks, D. J. Moss

Author Affiliations +

Proceedings Volume 11690, Smart Photonic and Optoelectronic Integrated Circuits XXIII; 116900M (2021) https://doi.org/10.1117/12.2584011
Event: SPIE OPTO, 2021, Online Only

Abstract

Optical artificial neural networks (ONNs) have significant potential for ultra-high computing speed and energy efficiency. We report a novel approach to ONNs that uses integrated Kerr optical micro-combs. This approach is programmable and scalable and is capable of reaching ultra-high speeds. We demonstrate the basic building block ONNs — a single neuron perceptron — by mapping synapses onto 49 wavelengths to achieve an operating speed of 11.9 x 109 operations per second, or Giga-OPS, at 8 bits per operation, which equates to 95.2 gigabits/s (Gbps). We test the perceptron on handwritten-digit recognition and cancer-cell detection — achieving over 90% and 85% accuracy, respectively. By scaling the perceptron to a deep learning network using off-the-shelf telecom technology we can achieve high throughput operation for matrix multiplication for real-time massive data processing.

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Citation Download Citation

M. Tan, X. Xu, J. Wu, A. Boes, B. Corcoran, T. G. Nguyen, S. T. Chu, B. E. Little, R. Morandotti, A. Mitchell, D. G. Hicks, and D. J. Moss "Single perceptron at 12 GigaOPs based on a microcomb for versatile, high-speed scalable, optical neural networks", Proc. SPIE 11690, Smart Photonic and Optoelectronic Integrated Circuits XXIII, 116900M (5 March 2021); https://doi.org/10.1117/12.2584011

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