Single photonic perceptron based on a soliton crystal Kerr microcomb for high-speed, scalable, optical neural networks

Understand

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.