Summary of Adaptive Layer Splitting For Wireless Llm Inference in Edge Computing: a Model-based Reinforcement Learning Approach, by Yuxuan Chen et al.
Adaptive Layer Splitting for Wireless LLM Inference in Edge Computing: A Model-Based Reinforcement Learning Approach
by Yuxuan Chen, Rongpeng Li, Xiaoxue Yu, Zhifeng Zhao, Honggang Zhang
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study optimizes the deployment of large language models (LLMs) in edge computing environments to enhance privacy and computational efficiency. The authors comprehensively analyze the impact of different splitting points in mainstream open-source LLMs and introduce a framework inspired by model-based reinforcement learning (MBRL) to determine the optimal splitting point across the edge and user equipment (UE). This approach reduces the computational cost of frequent performance evaluations by incorporating a reward surrogate model, achieving a balance between inference performance and computational load under varying network conditions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it possible for large language models to be used in devices like smartphones without needing a lot of power or data. The authors found that different ways of dividing the model into smaller parts affect how well it works on edge computing systems. They then developed a new approach using ideas from reinforcement learning to find the best way to split the model, which reduces the need for constant testing and makes it more efficient. |
Keywords
» Artificial intelligence » Inference » Reinforcement learning
