A Survey on Hardware Accelerators for Large Language Models
Fuente:
arXiv
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866915096449515520 |
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| author | Kachris, Christoforos |
| author_facet | Kachris, Christoforos |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_09890 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Survey on Hardware Accelerators for Large Language Models Kachris, Christoforos Hardware Architecture Computation and Language Machine Learning B.5; C.1; C.3 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. |
| title | A Survey on Hardware Accelerators for Large Language Models |
| topic | Hardware Architecture Computation and Language Machine Learning B.5; C.1; C.3 |
| url | https://arxiv.org/abs/2401.09890 |