DOTA: A Dynamically-Operated Photonic Tensor Core for Energy-Efficient Transformer Accelerator

Hanqing Zhu, Jiaqi Gu, Hanrui Wang, Zixuan Jiang, Zhekai Zhang, Rongxin Tang, Chenghao Feng, Song Han, Ray T. Chen, David Z. Pan

Abstract: The wide adoption and significant computing resource consumption of attention-based Transformers, e.g., Vision Transformer and large language models, have driven the demands for efficient hardware accelerators. While electronic accelerators have been commonly used, there is a growing interest in exploring photonics as an alternative technology due to its high energy efficiency and ultra-fast processing speed. Optical neural networks (ONNs) have demonstrated promising results for convolutional neural network (CNN) workloads that only require weight-static linear operations. However, they fail to efficiently support Transformer architectures with attention operations due to the lack of ability to process dynamic full-range tensor multiplication. In this work, we propose a customized high-performance and energy-efficient photonic Transformer accelerator, DOTA. To overcome the fundamental limitation of existing ONNs, we introduce a novel photonic tensor core, consisting of a crossbar array of interference-based optical vector dot-product engines, that supports highly-parallel, dynamic, and full-range matrix-matrix multiplication. Our comprehensive evaluation demonstrates that DOTA achieves a >4x energy and a >10x latency reduction compared to prior photonic accelerators, and delivers over 20x energy reduction and 2 to 3 orders of magnitude lower latency compared to the electronic Transformer accelerator. Our work highlights the immense potential of photonic computing for efficient hardware accelerators, particularly for advanced machine learning workloads.