Photonic Switched Optically Connected Memory: An Approach to Address Memory Challenges in Deep Learning

Deep learning has been revolutionizing many aspects of our society, powering various fields including computer vision, natural language processing, and activity recognition. However, the scaling trends for both datasets and model size are constraining system performance. Variability of memory requirements can lead to poor resource utilization. Reconfigurable photonic interconnects provide scalable solutions and enable efficient use of disaggregated memory resources. We propose a photonic switched optically connected memory system architecture that tackles the memory challenges while showing the functionality of optical switching for deep learning models. Our proposed system architecture utilizes a “lite” (de)serialization scheme for memory transfers via optical links to avoid network overheads and supports the dynamic allocation of remote memories to local processing systems. In order to test the feasibility of our proposal, we built an experimental testbed with a processing system and two remote memory nodes using silicon photonic switch fabrics and evaluated the system performance. The optical switching time is measured to be 119 μs and an overall 2.78 ms latency is achieved for the end-to-end reconfiguration. The collective results and existing high-bandwidth optical I/Os show the potential of integrating the photonic switched optically connected memory to state-of-the-art processing systems.