Single perceptron operating at 12 GigaOPs based on a Kerr soliton crystal microcomb for versatile, high-speed, scalable, optical neural networks
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 Authordmoss@swin.edu.au
ORCID of Submitting Author0000-0001-5195-1744
Submitting Author's InstitutionSwinburne University of Technology
Submitting Author's Country