Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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 (doi: 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 (doi: 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.

Subject Areas

microcombs; neural networks; optical neural networks; photonics

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