Seamless optical cloud computing across edge-metro network for generative AI

Sizhe Xing, Aolong Sun, Chengxi Wang, Yizhi Wang, Boyu Dong, Junhui Hu, Xuyu Deng, An Yan, Yinjun Liu, Fangchen Hu, Zhongya Li, Ouhan Huang, Junhao Zhao, Yingjun Zhou, Ziwei Li, Jianyang Shi, Xi Xiao, Richard Penty, Qixiang Cheng (), Nan Chi () and Junwen Zhang ()
Additional contact information
Sizhe Xing: Fudan University
Aolong Sun: Fudan University
Chengxi Wang: Fudan University
Yizhi Wang: University of Cambridge
Boyu Dong: Fudan University
Junhui Hu: Fudan University
Xuyu Deng: Fudan University
An Yan: Fudan University
Yinjun Liu: Fudan University
Fangchen Hu: Zhangjiang Laboratory
Zhongya Li: Fudan University
Ouhan Huang: Fudan University
Junhao Zhao: Fudan University
Yingjun Zhou: Fudan University
Ziwei Li: Fudan University
Jianyang Shi: Fudan University
Xi Xiao: National Information Optoelectronics Innovation Center
Richard Penty: University of Cambridge
Qixiang Cheng: University of Cambridge
Nan Chi: Fudan University
Junwen Zhang: Fudan University

Nature Communications, 2025, vol. 16, issue 1, 1-12

Abstract: Abstract The rapid advancement of generative artificial intelligence (AI) in recent years has profoundly reshaped modern lifestyles, necessitating a revolutionary architecture to support the growing demands for computational power. Cloud computing has become the driving force behind this transformation. However, it consumes significant power and faces computation security risks due to the reliance on extensive data centers and servers in the cloud. Reducing power consumption while enhancing computational scale remains persistent challenges in cloud computing. Here, we propose and experimentally demonstrate an optical cloud computing system that can be seamlessly deployed across edge-metro network. By modulating inputs and models into light, a wide range of edge nodes can directly access the optical computing center via the edge-metro network. The experimental validations show an energy efficiency of $$118.6$$ 118.6 mW/TOPs (tera operations per second), reducing energy consumption by two orders of magnitude compared to traditional electronic-based cloud computing solutions. Furthermore, it is experimentally validated that this architecture can perform various complex generative AI models through parallel computing to achieve image generation tasks.

Date: 2025
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DOI: 10.1038/s41467-025-61495-6

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