Optical switching for AI has moved from research to hyperscale deployment; yet the field is fragmenting fast. MEMS, silicon photonics, and LCoS-based OCS compete across incompatible tradeoff spaces, while AI workloads are diverging: training tolerates millisecond reconfiguration; inference demands sub-microsecond responsiveness that OCS alone cannot serve. Meanwhile, copper is rapidly giving way to optics across the intra- to inter-rack boundary, raising urgent questions about which switching paradigm and implementation technology gets deployed at scale. This workshop stages a direct confrontation between competing approaches, using training vs. inference requirements as the stress test.
Organizers
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Yi Lin
Huawei, China
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Liming Wang
Google, United States
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Shuangyi Yan
Univ. of Bristol, United Kingdom
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Ai Yanagihara
NTT, Japan
Speakers
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Vamsi Addank
Perdue University, United States
Talk: On novel optical switching
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Paraskevas Bakopoulos
Nvidia, United States
Talk: On modeling requirements
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Jinghui Li
Coherent , United States
Talk: On OCS roadmaps
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James Noraky
Google, United States
Talk: On modeling requirements
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George Zervas
Oriole Networks, United Kingdom
Talk: On novel optical switching
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Szilard Zsigmond
Lumenutm, United States
Talk: On OCS technologies