Seamless Optical Cloud Computing across Edge-Metro Network for Generative AI
This addresses power and security challenges in cloud computing for generative AI applications, though it appears incremental as it builds on optical computing concepts.
The paper tackles the high power consumption and security risks in cloud computing for generative AI by proposing an optical cloud computing system deployed across edge-metro networks, achieving an energy efficiency of 118.6 mW/TOPs and reducing energy consumption by two orders of magnitude compared to traditional electronic solutions.
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.