Photonic single perceptron at Giga-OP/s speeds with Kerr microcombs for scalable optical neural networks

TL;DR

This paper presents a scalable, ultrafast optical neural network using Kerr microcombs, demonstrating a single perceptron operating at Giga-OPS speeds and applying it to digit recognition and cancer detection.

Contribution

It introduces a novel Kerr microcomb-based approach for optical neural networks, achieving high speed, programmability, and scalability for real-time data processing.

Findings

Achieved 11.9 billion operations per second with a single perceptron.

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 novel approach to ONNs that uses integrated Kerr optical microcombs. This approach is programmable and scalable and is capable of reaching ultrahigh speeds. We demonstrate the basic building block ONNs, a single neuron perceptron, by mapping synapses onto 49 wavelengths to achieve an operating speed of 11.9 x 109 operations per second, or GigaOPS, at 8 bits per operation, which equates to 95.2 gigabits/s (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.