AOLO：面向低碳的无线大型语言模型服务分析与优化
AOLO: Analysis and Optimization For Low-Carbon Oriented Wireless Large Language Model Services
AI总结：
本文提出AOLO框架，通过量化计算推理与无线通信全链路碳足迹并联合优化推理输出与发射功率，利用基于SNN的深度强化学习算法，显著降低了无线LLM服务的总体碳足迹。
摘要：
大型语言模型（LLMs）的最新进展促使其在各个领域得到广泛采用和大规模部署。然而，由于其巨大的能耗和碳足迹，它们对环境的影响，特别是在推理期间，已成为日益受到关注的问题。现有研究仅关注推理计算，忽略了对网络辅助LLM服务系统中碳足迹的分析和优化。为了填补这一空白，我们提出了AOLO，一个面向低碳的无线LLM服务分析与优化框架。AOLO引入了一个全面的碳足迹模型，用于量化整个LLM服务链中的温室气体排放，包括计算推理和无线通信。此外，我们提出了一个旨在最小化总体碳足迹的优化问题，该问题通过在体验质量和系统性能约束下联合优化推理输出和发射功率来解决。为了实现这种联合优化，我们利用脉冲神经网络（SNNs）的能效，采用SNN作为行动者网络，并提出了一种面向低碳的优化算法，即基于SNN的深度强化学习（SDRL）。综合仿真表明，SDRL算法显著减少了总体碳足迹，与基准soft actor-critic相比减少了18.77%，突显了其在实现更可持续LLM推理服务方面的潜力。
英文摘要：
Recent advancements in large language models (LLMs) have led to their widespread adoption and large-scale deployment across various domains. However, their environmental impact, particularly during inference, has become a growing concern due to their substantial energy consumption and carbon footprint. Existing research has focused on inference computation alone, overlooking the analysis and optimization of carbon footprint in network-aided LLM service systems. To address this gap, we propose AOLO, a framework for analysis and optimization for low-carbon oriented wireless LLM services. AOLO introduces a comprehensive carbon footprint model that quantifies greenhouse gas emissions across the entire LLM service chain, including computational inference and wireless communication. Furthermore, we formulate an optimization problem aimed at minimizing the overall carbon footprint, which is solved through joint optimization of inference outputs and transmit power under quality-of-experience and system performance constraints. To achieve this joint optimization, we leverage the energy efficiency of spiking neural networks (SNNs) by adopting SNN as the actor network and propose a low-carbon-oriented optimization algorithm, i.e., SNN-based deep reinforcement learning (SDRL). Comprehensive simulations demonstrate that SDRL algorithm significantly reduces overall carbon footprint, achieving an 18.77% reduction compared to the benchmark soft actor-critic, highlighting its potential for enabling more sustainable LLM inference services.
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