An array of microresonators as a Photonic Extreme Learning Machine

TL;DR

This paper demonstrates a photonic extreme learning machine implemented on a silicon chip, utilizing microresonator arrays for fast, parallel optical processing and simplified training for machine learning tasks.

Contribution

It presents the first experimental implementation of a photonic extreme learning machine using integrated microresonators for optical neural network processing.

Findings

Successfully classified binary and analog tasks.

Performance improves with more microresonators.

Proof-of-concept validation of photonic neural network.

Abstract

Machine learning technologies have found fertile ground in optics due to its promising features based on speed and parallelism. Feed-forward neural networks are one of the most widely used machine learning algorithms due to their simplicity and universal approximation capability. However, the typical training procedure, where all weights are optimized, can be time and energy consuming. An alternative approach is the Extreme Learning Machine, a feed-forward neural network in which only the output weights are trained, while the internal connections are random. Here we present an experimental implementation of a photonic extreme learning machine (PELM) in an integrated silicon chip. The PELM is based on the processing of the image of the scattered light by an array of 18 gratings coupled to microresonators. Light propagation in the microresonator array is a linear process while light detection by the video camera is a nonlinear process. Training is done offline by analyzing the recorded scattered light image with a linear classifier. We provide a proof-of-concept demonstration of the PELM by solving both binary and analog tasks, and show how the performance depends on the number of microresonators used in the readout procedure.