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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2410.11845 |
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| _version_ | 1866910839941890048 |
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| author | Zheng, Yue Chen, Yuhao Qian, Bin Shi, Xiufang Shu, Yuanchao Chen, Jiming |
| author_facet | Zheng, Yue Chen, Yuhao Qian, Bin Shi, Xiufang Shu, Yuanchao Chen, Jiming |
| contents | Large language models (LLMs) have revolutionized natural language processing with their exceptional understanding, synthesizing, and reasoning capabilities. However, deploying LLMs on resource-constrained edge devices presents significant challenges due to computational limitations, memory constraints, and edge hardware heterogeneity. This survey provides a comprehensive overview of recent advancements in edge LLMs, covering the entire lifecycle: from resource-efficient model design and pre-deployment strategies to runtime inference optimizations. It also explores on-device applications across various domains. By synthesizing state-of-the-art techniques and identifying future research directions, this survey bridges the gap between the immense potential of LLMs and the constraints of edge computing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_11845 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Review on Edge Large Language Models: Design, Execution, and Applications Zheng, Yue Chen, Yuhao Qian, Bin Shi, Xiufang Shu, Yuanchao Chen, Jiming Distributed, Parallel, and Cluster Computing Large language models (LLMs) have revolutionized natural language processing with their exceptional understanding, synthesizing, and reasoning capabilities. However, deploying LLMs on resource-constrained edge devices presents significant challenges due to computational limitations, memory constraints, and edge hardware heterogeneity. This survey provides a comprehensive overview of recent advancements in edge LLMs, covering the entire lifecycle: from resource-efficient model design and pre-deployment strategies to runtime inference optimizations. It also explores on-device applications across various domains. By synthesizing state-of-the-art techniques and identifying future research directions, this survey bridges the gap between the immense potential of LLMs and the constraints of edge computing. |
| title | A Review on Edge Large Language Models: Design, Execution, and Applications |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2410.11845 |