Soliton crystal Kerr microcombs for high-speed, scalable optical neural networks at 10 GigaOPs/s

TL;DR

This paper introduces a scalable, high-speed optical neural network using Kerr micro-combs, achieving 11.9 Giga-OPS per neuron and demonstrating practical applications in digit recognition and cancer detection.

Contribution

It presents a novel, programmable Kerr micro-comb-based ONN architecture capable of ultra-high-speed processing and scalability for real-time data analysis.

Findings

Achieved 11.9 Giga-OPS per neuron at 8 bits per operation.

Demonstrated over 90% accuracy in digit recognition.

Enabled high-throughput matrix multiplication for deep learning.

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.