期刊
LASER & PHOTONICS REVIEWS
卷 14, 期 10, 页码 -出版社
WILEY-V C H VERLAG GMBH
DOI: 10.1002/lpor.202000070
关键词
Kerr micro-comb; machine learning; optical neural networks; photonic perceptron
资金
- Australian Research Council Discovery Projects Program [DP150104327]
- Natural Sciences and Engineering Research Council of Canada (NSERC)
- MESI PSR-SIIRI Initiative in Quebec
- Canada Research Chair Program
- Strategic Priority Research Program of the Chinese Academy of Sciences [XDB24030000]
- Australian Research Council [FT104101104]
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.
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