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A Survey on Hardware Accelerators for Large Language Models

Christoforos Kachris
University of West Attica, Greece
arXiv:2401.09890 [cs.AR], (18 Jan 2024)

@misc{kachris2024survey,

   title={A Survey on Hardware Accelerators for Large Language Models},

   author={Christoforos Kachris},

   year={2024},

   eprint={2401.09890},

   archivePrefix={arXiv},

   primaryClass={cs.AR}

}

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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.
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