In the rapidly evolving landscape of artificial intelligence, large language models (LLMs) and graph processing have emerged as transformative technologies for natural language processing (NLP), computer vision, and graph-structured data applications. However, the complex structures of these models pose challenges for acceleration on conventional electronic platforms. In this paper, we describe novel hardware accelerators based on silicon photonics to accelerate transformer neural networks that are used in LLMs and graph neural networks for graph data processing. Our analysis demonstrates that both hardware accelerators achieve at least 10.2x throughput improvement and 3.8x better energy efficiency over multiple state-of-the-art electronic hardware accelerators designed for LLMs and graph processing.

翻译：在人工智能快速发展的背景下，大型语言模型和图处理已成为自然语言处理、计算机视觉及图结构数据应用中的变革性技术。然而，这些模型的复杂结构给传统电子平台上的加速带来了挑战。本文描述了一种基于硅光子学的新型硬件加速器，用于加速大型语言模型中采用的Transformer神经网络以及图数据处理中的图神经网络。我们的分析表明，与多种专为大型语言模型和图处理设计的最先进电子硬件加速器相比，两种硬件加速器均实现了至少10.2倍的吞吐量提升和3.8倍的能效改进。