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

TL;DR

This paper introduces a scalable, high-speed optical neural network architecture using integrated Kerr micro-comb sources, demonstrating a single perceptron with high throughput and accuracy on benchmark tasks, paving the way for real-time neuromorphic computing.

Contribution

It presents a novel integrated micro-comb based perceptron architecture that is programmable, scalable, and capable of ultra-high-speed operation for optical neural networks.

Findings

Achieved 11.9 Giga-FLOPS throughput per neuron.

Demonstrated over 90% accuracy on handwritten-digit recognition.

Utilized record small wavelength spacing (49GHz) for micro-comb source.

Abstract

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.