用稀疏神经电路策略估算基站能耗，更省电更稳定。
Towards Green AI-Native Networks: Evaluation of Neural Circuit Policy for Estimating Energy Consumption of Base Stations
- 采用稀疏结构的神经电路策略降低计算和能耗。
- 相比LSTM等模型，计算开销减少，且对超参数变化不敏感。
- 适合电信领域MLOps场景，兼顾节能与模型管理便利性。
无线接入网中，优化射频硬件与基于AI的网络管理软件可显著节省能源。然而，支撑节能决策的底层机器学习（ML）模型本身可能带来额外计算与能耗，因此探索高效、低耗的ML技术至关重要。本文评估了新型稀疏神经电路策略（NCPs）在基站能耗预测任务中的应用。稀疏结构能降低内存、计算与能耗需求，实现低成本、可扩展的解决方案。通过对比长期短期记忆网络（LSTM）等传统模型，量化分析了不同超参数（如训练轮数、每层神经元数）对性能的影响。结果表明，NCPs在计算开销和能耗上均有明显降低；同时对超参数变化具有强鲁棒性，有利于简化模型管理，降低电信领域机器学习运维（MLOps）的能耗负担。
原文摘要 · Abstract (English)
Optimization of radio hardware and AI-based network management software yield significant energy savings in radio access networks. The execution of underlying Machine Learning (ML) models, which enable energy savings through recommended actions, may require additional compute and energy, highlighting the opportunity to explore and adopt accurate and energy-efficient ML technologies. This work evaluates the novel use of sparsely structured Neural Circuit Policies (NCPs) in a use case to estimate the energy consumption of base stations. Sparsity in ML models yields reduced memory, computation and energy demand, hence facilitating a low-cost and scalable solution. We also evaluate the generalization capability of NCPs in comparison to traditional and widely used ML models such as Long Short Term Memory (LSTM), via quantifying their sensitivity to varying model hyper-parameters (HPs). NCPs demonstrated a clear reduction in computational overhead and energy consumption. Moreover, results indicated that the NCPs are robust to varying HPs such as number of epochs and neurons in each layer, making them a suitable option to ease model management and to reduce energy consumption in Machine Learning Operations (MLOps) in telecommunications.
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