论文标题
基于VCSEL的光学互连的端到端学习：最新，挑战和机遇
End-to-End Learning for VCSEL-based Optical Interconnects: State-of-the-Art, Challenges, and Opportunities
论文作者
论文摘要
基于垂直腔表面发射激光器（VCSELS）的光学互连（OIS）是数据中心，超级计算机甚至车辆的主要主力，可提供低成本，高速公路连接。 VCSEL必须在极度苛刻和时变的条件下进行操作，从而需要对通信链的适应性和灵活的设计。可以基于数学模型（基于模型的设计）或从基于数据的设计（ML）设计中学到的此类设计。最近，各种ML技术都走到了最前沿，用深层神经网络代替了发射器和接收器中的单个组件。除了这样的组件学习之外，端到端（E2E）自动编码器方法还可以通过将整个参数化发射机和接收器进行优化来达到最终性能。该教程论文旨在为基于VCSEL的OI提供ML的概述，重点是E2E方法，特别涉及VCSELS面临的独特挑战，例如广泛的温度变化和复杂的模型。
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.
