Xing Sizhe, Sun Aolong, Wang Chengxi, Wang Yizhi, Dong Boyu, Hu Junhui, Deng Xuyu, Yan An, Liu Yinjun, Hu Fangchen, Li Zhongya, Huang Ouhan, Zhao Junhao, Zhou Yingjun, Li Ziwei, Shi Jianyang, Xiao Xi, Penty Richard, Cheng Qixiang, Chi Nan, Zhang Junwen
School of Information Science and Technology, Fudan University, Shanghai, China.
Centre for Photonic Systems, Electrical Engineering Division, Department of Engineering, University of Cambridge, Cambridge, CB3 0FA, UK.
Nat Commun. 2025 Jul 2;16(1):6097. doi: 10.1038/s41467-025-61495-6.
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 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.
近年来，生成式人工智能（AI）的迅速发展深刻地重塑了现代生活方式，这就需要一种革命性的架构来支持对计算能力日益增长的需求。云计算已成为这一转变的驱动力。然而，由于依赖云中大量的数据中心和服务器，云计算消耗大量电力并面临计算安全风险。在提高计算规模的同时降低功耗仍然是云计算中持续存在的挑战。在此，我们提出并通过实验证明了一种可以无缝部署在边缘-城域网络中的光学云计算系统。通过将输入和模型调制为光，广泛的边缘节点可以通过边缘-城域网络直接访问光学计算中心。实验验证表明，其能效为mW/TOPs（每秒万亿次运算），与传统的基于电子的云计算解决方案相比，能耗降低了两个数量级。此外，通过实验验证，该架构可以通过并行计算执行各种复杂的生成式AI模型，以完成图像生成任务。
