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.

在人工智能快速发展的领域中，大型语言模型（LLMs）和图处理已经成为自然语言处理（NLP）、计算机视觉和图结构数据应用的变革性技术。本文描述了基于硅光子学的新型硬件加速器，用于加速在LLMs和图神经网络中使用的Transformer神经网络进行图数据处理。我们的分析表明，这两种硬件加速器在吞吐量上至少实现了10.2倍的提升，并且在能源效率上比多个用于LLMs和图处理的最新电子硬件加速器提高了3.8倍。

利用硅光子技术加速大型语言模型和图处理的神经网络