Artificial Intelligence (AI) demands large data flows within datacenters, heavily relying on multicasting data transfers. As AI models scale, the requirement for high-bandwidth and low-latency networking compounds. The common use of electrical packet switching faces limitations due to optical-electrical-optical conversion bottlenecks. Optical switches, while bandwidth-agnostic and low-latency, suffer from having only unicast or non-scalable multicasting capability. This paper introduces an optical switching technique addressing this challenge. Our approach enables arbitrarily programmable simultaneous unicast and multicast connectivity, eliminating the need for optical splitters that hinder scalability due to optical power loss. We use phase modulation in multiple layers, tailored to implement any multicast connectivity map. Phase modulation also enables wavelength selectivity on top of spatial selectivity, resulting in an optical switch that implements space-wavelength routing. We conducted simulations and experiments to validate our approach. Our results affirm the concept's feasibility, effectiveness, and scalability, as a multicasting switch by experimentally demonstrating 16 spatial ports using 2 wavelength channels. Numerically, 64 spatial ports with 4 wavelength channels each were simulated, with approximately constant efficiency (< 3 dB) as ports and wavelength channels scale.

翻译：人工智能（AI）对数据中心内的大数据流有极高需求，且高度依赖多播数据传输。随着AI模型规模扩大，对高带宽、低延迟网络的需求进一步加剧。常见的电分组交换因光电-电光转换瓶颈而面临局限。光交换机虽具有带宽无关性和低延迟特性，但仅支持单播或不可扩展的多播能力。本文提出一种光学交换技术以解决该挑战。我们的方法可实现任意可编程的同步单播与多播连接，无需使用因光功率损耗而阻碍扩展性的光分路器。我们采用多层相位调制技术，可定制实现任意多播连接映射。相位调制还支持在空间选择性基础上叠加波长选择性，从而构建实现空间-波长路由的光学交换机。通过仿真与实验验证该方案，结果证实了其作为多播交换机的可行性、有效性及可扩展性：实验演示了利用两个波长通道的16个空间端口，数值仿真则模拟了每个端口支持4个波长通道的64个空间端口，且随着端口与波长通道扩展，效率基本保持恒定（<3 dB）。