提出能耗会计框架，解析智能体推理中计算与通信的双重耗能问题
Networking-Aware Energy Efficiency in Agentic AI Inference: A Survey
- 构建感知-推理-行动全链路能耗会计框架
- 揭示计算与通信共同构成智能体推理主要能耗瓶颈
- 适合研究绿色智能体、边缘计算与6G智能系统的读者
大语言模型（LLMs）的快速发展推动了智能体人工智能（Agentic AI）的兴起，这类自主系统将感知、推理与行动整合为闭环流程以实现持续适应。尽管在移动边缘计算、自动驾驶系统和下一代无线网络中带来变革性应用，该范式因迭代推理和持续数据交换带来了根本性的能耗挑战。与传统AI以浮点运算（FLOPs）为瓶颈不同，智能体AI面临计算与通信能耗叠加的问题。本文提出一种能耗会计框架，识别感知-推理-行动循环中的计算与通信成本，建立涵盖模型简化、计算控制、输入与注意力优化、硬件感知推理的统一分类体系。探索跨层协同设计策略，联合优化模型参数、无线传输与边缘资源。最后，指出联邦绿色学习、碳感知智能体、6G原生智能体与自维持系统等开放挑战，为可扩展自主智能提供路线图。
原文摘要 · Abstract (English)
The rapid emergence of Large Language Models (LLMs) has catalyzed Agentic artificial intelligence (AI), autonomous systems integrating perception, reasoning, and action into closed-loop pipelines for continuous adaptation. While unlocking transformative applications in mobile edge computing, autonomous systems, and next-generation wireless networks, this paradigm creates fundamental energy challenges through iterative inference and persistent data exchange. Unlike traditional AI where bottlenecks are computational Floating Point Operations (FLOPs), Agentic AI faces compounding computational and communication energy costs. In this survey, we propose an energy accounting framework identifying computational and communication costs across the Perception-Reasoning-Action cycle. We establish a unified taxonomy spanning model simplification, computation control, input and attention optimization, and hardware-aware inference. We explore cross-layer co-design strategies jointly optimizing model parameters, wireless transmissions, and edge resources. Finally, we identify open challenges of federated green learning, carbon-aware agency, 6th generation mobile communication (6G)-native Agentic AI, and self-sustaining systems, providing a roadmap for scalable autonomous intelligence.
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