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