Accelerating Neural Networks for Large Language Models and Graph Processing with Silicon Photonics
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866910296080121856 |
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| author | Afifi, Salma Sunny, Febin Nikdast, Mahdi Pasricha, Sudeep |
| author_facet | Afifi, Salma Sunny, Febin Nikdast, Mahdi Pasricha, Sudeep |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_06885 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Accelerating Neural Networks for Large Language Models and Graph Processing with Silicon Photonics Afifi, Salma Sunny, Febin Nikdast, Mahdi Pasricha, Sudeep Hardware Architecture Machine Learning 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. |
| title | Accelerating Neural Networks for Large Language Models and Graph Processing with Silicon Photonics |
| topic | Hardware Architecture Machine Learning |
| url | https://arxiv.org/abs/2401.06885 |