用空中计算支持大模型在多场景实时运行，提升应急响应效率
LLMs are everywhere: Ubiquitous Utilization of AI Models through Air Computing
- 将无人机等空中平台组成三维计算网络，协同运行大模型任务
- 通过空中单元卸载计算负载，显著改善服务响应与体验质量
- 适用于灾害救援等关键场景，实现智能决策与高效协同
我们正进入一个新阶段，大型语言模型（LLMs）被广泛应用于代码生成、假期规划等各类认知任务。这一趋势对跨多种环境的通用性执行提出需求，传统二维地面网络基础设施可能难以满足。一种有前景的解决方案是将边缘计算拓展至三维空间，利用多层空中平台构成空域计算（air computing）体系，为本地设备提供大模型与生成式AI（GenAI）应用的计算支持。该方案通过将计算任务卸载至无人机等空中单元，缓解现有基础设施压力，同时提升服务效率。此外，多类型空中单元的协同部署可显著提升用户体验质量（QoE），确保任务执行的无缝性、自适应性和鲁棒性。本文研究了基于大模型的应用与空域计算的融合潜力，并以灾害应对为例，展示二者协同如何显著改善紧急情况下的响应效果。
原文摘要 · Abstract (English)
We are witnessing a new era where problem-solving and cognitive tasks are being increasingly delegated to Large Language Models (LLMs) across diverse domains, ranging from code generation to holiday planning. This trend also creates a demand for the ubiquitous execution of LLM-powered applications in a wide variety of environments in which traditional terrestrial 2D networking infrastructures may prove insufficient. A promising solution in this context is to extend edge computing into a 3D setting to include aerial platforms organized in multiple layers, a paradigm we refer to as air computing, to augment local devices for running LLM and Generative AI (GenAI) applications. This approach alleviates the strain on existing infrastructure while enhancing service efficiency by offloading computational tasks to the corresponding air units such as UAVs. Furthermore, the coordinated deployment of various air units can significantly improve the Quality of Experience (QoE) by ensuring seamless, adaptive, and resilient task execution. In this study, we investigate the synergy between LLM-based applications and air computing, exploring their potential across various use cases. Additionally, we present a disaster response case study demonstrating how the collaborative utilization of LLMs and air computing can significantly improve outcomes in critical situations.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。