Scalable Coherent Optical Crossbar Architecture using PCM for AI Acceleration
Dan Sturm, Sajjad Moazeni · 2023
Optical computing has recently been proposed as a new compute paradigm to meet the demands of future AI/ML workloads in datacenters and supercomputers. However, proposed implementations so far suffer from lack of scalability, large footprints and high power consumption, and incomplete system-level architectures inhibit integration within existing datacenter systems for real-world applications. In this work, we present a truly scalable optical AI accelerator based on a crossbar architecture. We have considered all major roadblocks and address them in this design. Weights will be stored on-chip using phase change material (PCM) that can be monolithically integrated in silicon photonic processes. All electro-optical components and circuit blocks are modeled based on measured performance metrics in a 45nm monolithic silicon photonic process, which can be co-packaged with advanced CPU/GPUs and HBM memories. We also present a system-level modeling and analysis of our chip's performance for the Resnet-50V1.5, considering all critical parameters, including memory size, array size, photonic losses, and energy consumption of peripheral electronics. Both on-chip SRAM and off-chip DRAM energy overheads have been considered in this modeling. We additionally address how using a dual-core crossbar design can eliminate programming time overhead at practical SRAM block sizes and batch sizes. Our results show that a 128 × 128 proposed architecture can achieve inference per second (IPS) similar to Nvidia A100 GPU at 15.4× lower power and 7.24× lower area.