Programmable Photonic Extreme Learning Machines

TL;DR

This paper demonstrates a programmable photonic extreme learning machine (PPELM) that performs complex classification tasks efficiently, leveraging on-chip programmability, integrated nonlinearity, and novel techniques to enhance accuracy and reduce variability.

Contribution

The authors experimentally implement a programmable photonic ELM with on-chip input, hidden layer, and nonlinearity, and introduce techniques to improve model accuracy and stability.

Findings

Successfully classified three complex tasks.

Achieved high accuracy with evolutionary and wavelength multiplexing techniques.

Demonstrated feasibility of on-chip training for photonic neural networks.

Abstract

Photonic neural networks offer a promising alternative to traditional electronic systems for machine learning accelerators due to their low latency and energy efficiency. However, the challenge of implementing the backpropagation algorithm during training has limited their development. To address this, alternative machine learning schemes, such as extreme learning machines (ELMs), have been proposed. ELMs use a random hidden layer to increase the feature space dimensionality, requiring only the output layer to be trained through linear regression, thus reducing training complexity. Here, we experimentally demonstrate a programmable photonic extreme learning machine (PPELM) using a hexagonal waveguide mesh, and which enables to program directly on chip the input feature vector and the random hidden layer. Our system also permits to apply the nonlinearity directly on-chip by using the systems integrated photodetecting elements. Using the PPELM we solved successfully three different complex classification tasks. Additioanlly, we also propose and demonstrate two techniques to increase the accuracy of the models and reduce their variability using an evolutionary algorithm and a wavelength division multiplexing approach, obtaining excellent performance. Our results show that programmable photonic processors may become a feasible way to train competitive machine learning models on a versatile and compact platform.