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• W3128806918 abstract "<p><b>Optical artificial neural networks (ONNs) — analog computing hardware tailored for machine learning — have significant potential for ultra-high computing speed and energy efficiency. We propose a new approach to architectures for ONNs based on integrated Kerr micro-comb sources 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. </b></p>" @default.
• W3128806918 created "2021-02-15" @default.
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• W3128806918 date "2020-03-09" @default.
• W3128806918 modified "2023-09-23" @default.
• W3128806918 title "Photonic perceptron based on a Kerr microcomb for high-speed, scalable, optical neural networks" @default.
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• W3128806918 doi "https://doi.org/10.36227/techrxiv.11925225.v1" @default.
• W3128806918 hasPublicationYear "2020" @default.
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