通过量化与剪枝平衡能耗与性能，提升通信网络故障分析效率
Energy-Aware LLMs: A step towards sustainable AI for downstream applications
- 构建端到端节能管道，权衡模型性能与能耗
- 在真实数据集上实现能耗降低同时性能显著提升
- 适合关注AI可持续性与通信系统优化的研究者
先进的大语言模型（LLMs）已深刻变革通信网络等领域，推动了诸多创新应用与解决方案的升级。然而，多数LLMs依赖巨大计算资源，导致能源消耗惊人。本研究提出一个端到端的节能框架，探索在通信网络故障票务分析中，模型性能与能效之间的权衡。通过两个真实世界数据集，评估该框架在根因分析与响应反馈任务中的表现。结果表明，合理组合量化与剪枝技术可在显著降低能耗的同时，大幅提升模型性能。
原文摘要 · Abstract (English)
Advanced Large Language Models (LLMs) have revolutionized various fields, including communication networks, sparking an innovation wave that has led to new applications and services, and significantly enhanced solution schemes. Despite all these impressive developments, most LLMs typically require huge computational resources, resulting in terribly high energy consumption. Thus, this research study proposes an end-to-end pipeline that investigates the trade-off between energy efficiency and model performance for an LLM during fault ticket analysis in communication networks. It further evaluates the pipeline performance using two real-world datasets for the tasks of root cause analysis and response feedback in a communication network. Our results show that an appropriate combination of quantization and pruning techniques is able to reduce energy consumption while significantly improving model performance.
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