SPIE PW2021 Paper 11690-21 perceptron IEEE Techrxiv.pdf (1.93 MB)
Download file

Single perceptron operating at 12 GigaOPs based on a Kerr soliton crystal microcomb for versatile, high-speed, scalable, optical neural networks

Download (1.93 MB)
posted on 2021-03-01, 16:57 authored by David MossDavid Moss

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 micro-combs. This approach is programmable and scalable and is capable of reaching ultra-high 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 Giga-OPS, 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.


Email Address of Submitting Author

ORCID of Submitting Author


Submitting Author's Institution

Swinburne University of Technology

Submitting Author's Country

  • Australia

Usage metrics