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.

本研究解决了无线接入网络中能耗估计的挑战，提出了一种新颖的稀疏结构神经电路政策（NCP），以提高能源效率。研究表明，NCPs在降低计算开销和能耗方面效果显著，且对模型超参数变化具有较强的鲁棒性，为电信行业的机器学习操作提供了一种低成本且可扩展的解决方案。

朝着绿色人工智能本地网络的目标：神经电路政策对基站能耗估计的评估