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.

翻译：以垂直孔径表面发射激光(VCSELs)为基础的光学互连(OIs)是数据中心、超级计算机甚至车辆的主要工作马,提供低成本、高速度的连通。VCSELs必须在极其苛刻和时间变化的条件下运作,因此需要对通信链进行适应性和灵活的设计。这种设计可以基于数学模型(基于模型的设计)或从数据(基于机械学习(ML)的设计)中学习。各种ML技术最近来到了前沿,用深神经网络取代了发射机和接收器中的个别部件。除了这种组件学习之外,终端到终端(E2E)自动电算器的方法可以通过共同优化整个参数化的发射机和接收器达到最终性能。这份指导文件旨在为基于VCSEL的OI提供ML概要,重点是E2E方法,特别是处理VCSELs所面临的独特挑战,例如广泛的温度变化和复杂模型。