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Soliton Crystal Microcombs for Versatile, High-Speed, Scalable Optical Neural Networks
Version 1
: Received: 5 November 2020 / Approved: 6 November 2020 / Online: 6 November 2020 (09:19:13 CET)
How to cite: Xu, X.; Tan, M.; Moss, D. Soliton Crystal Microcombs for Versatile, High-Speed, Scalable Optical Neural Networks. Preprints 2020, 2020110233. https://doi.org/10.20944/preprints202011.0233.v1 Xu, X.; Tan, M.; Moss, D. Soliton Crystal Microcombs for Versatile, High-Speed, Scalable Optical Neural Networks. Preprints 2020, 2020110233. https://doi.org/10.20944/preprints202011.0233.v1
Abstract
Optical artificial neural networks (ONNs) have significant potential for ultra-high computing speed and energy efficiency. We report a new approach to ONNs based on integrated Kerr micro-combs that is programmable, highly scalable and capable of reaching ultra-high speeds, demonstrating the building block of the ONN — a single neuron perceptron — by mapping synapses onto 49 wavelengths to achieve a single-unit throughput of 11.9 Giga-OPS at 8 bits per OP, or 95.2 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.
Keywords
microcombs; neural networks; optical neural networks; photonics
Subject
Physical Sciences, Optics and Photonics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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