用光计算替代电子云，实现低能耗的生成式AI部署
Seamless Optical Cloud Computing across Edge-Metro Network for Generative AI
- 将输入与模型调制成光信号，在边缘-城域网中实现光计算
- 实测能效达118.6毫瓦/万亿次操作，比传统方案低两个数量级
- 支持多种生成式AI模型并行运行，适合大规模图像生成任务
近年来生成式人工智能的快速发展深刻改变了现代生活方式，亟需革命性的架构来支撑日益增长的算力需求。云计算已成为这一变革的核心驱动力，但其依赖大规模数据中心和服务器，导致功耗高且存在计算安全风险。降低功耗同时提升计算规模仍是云计算的长期挑战。本文提出并实验验证了一种可在边缘-城域网络中无缝部署的光学云计算系统。通过将输入和模型调制为光信号，大量边缘节点可经由边缘-城域网络直接接入光学计算中心。实验验证显示，该系统的能效达118.6 mW/TOPs（万亿次操作每秒），相比传统电子云方案降低两个数量级。此外，该架构已成功实现多种复杂生成式AI模型的并行计算，完成图像生成任务。
原文摘要 · Abstract (English)
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 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.
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