A Survey on Hardware Accelerators for Large Language Models

• URL: http://arxiv.org/abs/2401.09890v1
• Date: Thu, 18 Jan 2024 11:05:03 GMT
• Title: A Survey on Hardware Accelerators for Large Language Models
• Authors: Christoforos Kachris
• Abstract summary: Large Language Models (LLMs) have emerged as powerful tools for natural language processing tasks. There is a pressing need to address the computational challenges associated with their scale and complexity.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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