Large Language Models (LLMs) have emerged as powerful tools for natural language processing tasks, revolutionizing the field with their ability to understand and generate human-like text. As the demand for more sophisticated LLMs continues to grow, there is a pressing need to address the computational challenges associated with their scale and complexity. This paper presents a comprehensive survey on hardware accelerators designed to enhance the performance and energy efficiency of Large Language Models. By examining a diverse range of accelerators, including GPUs, FPGAs, and custom-designed architectures, we explore the landscape of hardware solutions tailored to meet the unique computational demands of LLMs. The survey encompasses an in-depth analysis of architecture, performance metrics, and energy efficiency considerations, providing valuable insights for researchers, engineers, and decision-makers aiming to optimize the deployment of LLMs in real-world applications.

翻译：大语言模型（LLMs）已成为自然语言处理领域的强大工具，其理解与生成类人文本的能力彻底革新了该领域。随着对更复杂LLMs需求的持续增长，应对其规模与复杂性带来的计算挑战已迫在眉睫。本文对专为提升大语言模型性能与能效而设计的硬件加速器进行了全面综述。通过考察包括GPU、FPGA及定制架构在内的多样化加速器，我们深入探索了针对LLMs独特计算需求定制的硬件解决方案全景。本综述涵盖了对架构、性能指标及能效考量的深入分析，为致力于优化LLMs在真实世界应用中部署的研究人员、工程师及决策者提供了宝贵见解。