End-to-End Learning for VCSEL-based Optical Interconnects: State-of-the-Art, Challenges, and Opportunities

TL;DR

This paper reviews the application of end-to-end machine learning approaches, especially autoencoders, for optimizing VCSEL-based optical interconnects in data centers and vehicles, addressing challenges like temperature variability.

Contribution

It provides a comprehensive overview of ML techniques, focusing on end-to-end autoencoder methods, for improving VCSEL-based optical interconnects under challenging conditions.

Findings

End-to-end ML approaches can optimize entire communication systems.

Autoencoder models address VCSEL temperature variations.

ML techniques outperform traditional design methods.

Abstract

Optical interconnects (OIs) based on vertical-cavity surface-emitting lasers (VCSELs) are the main workhorse within data centers, supercomputers, and even vehicles, providing low-cost, high-rate connectivity. VCSELs must operate under extremely harsh and time-varying conditions, thus requiring adaptive and flexible designs of the communication chain. Such designs can be built based on mathematical models (model-based design) or learned from data (machine learning (ML) based design). Various ML techniques have recently come to the forefront, replacing individual components in the transmitters and receivers with deep neural networks. Beyond such component-wise learning, end-to-end (E2E) autoencoder approaches can reach the ultimate performance through co-optimizing entire parameterized transmitters and receivers. This tutorial paper aims to provide an overview of ML for VCSEL-based OIs, with a focus on E2E approaches, dealing specifically with the unique challenges facing VCSELs, such as the wide temperature variations and complex models.