将大模型引入边缘计算，打造更智能的本地化系统。
Towards Edge General Intelligence via Large Language Models: Opportunities and Challenges
- 提出三种基于大模型的边缘通用智能架构：集中式、混合式、去中心化。
- 评测多种小语言模型在边缘设备上的性能与吞吐量表现。
- 为边缘智能研究者提供系统性框架与未来方向参考。
边缘智能（EI）通过利用边缘网络的计算能力，实现了实时、本地化的服务。大型语言模型（LLMs）的引入使边缘智能迈向新阶段——边缘通用智能（EGI），支持需要高级理解与推理能力的自适应、多样化应用。然而，该领域尚缺乏系统性探索。本文区分了EGI与传统EI的差异，将基于大模型的EGI划分为三类概念系统：集中式、混合式与去中心化，并详细阐述各类系统的框架设计与现有实现。同时，评估了多种更适合边缘设备部署的小型语言模型（SLMs）在性能与吞吐量方面的表现。本综述为研究人员提供了关于EGI的全面视角，揭示其巨大潜力，并为这一快速发展的领域奠定未来研究基础。
原文摘要 · Abstract (English)
Edge Intelligence (EI) has been instrumental in delivering real-time, localized services by leveraging the computational capabilities of edge networks. The integration of Large Language Models (LLMs) empowers EI to evolve into the next stage: Edge General Intelligence (EGI), enabling more adaptive and versatile applications that require advanced understanding and reasoning capabilities. However, systematic exploration in this area remains insufficient. This survey delineates the distinctions between EGI and traditional EI, categorizing LLM-empowered EGI into three conceptual systems: centralized, hybrid, and decentralized. For each system, we detail the framework designs and review existing implementations. Furthermore, we evaluate the performance and throughput of various Small Language Models (SLMs) that are more suitable for development on edge devices. This survey provides researchers with a comprehensive vision of EGI, offering insights into its vast potential and establishing a foundation for future advancements in this rapidly evolving field.
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