This work presents a novel approach to neural architecture search (NAS) that aims to reduce energy costs and increase carbon efficiency during the model design process. The proposed framework, called carbon-efficient NAS (CE-NAS), consists of NAS evaluation algorithms with different energy requirements, a multi-objective optimizer, and a heuristic GPU allocation strategy. CE-NAS dynamically balances energy-efficient sampling and energy-consuming evaluation tasks based on current carbon emissions. Using a recent NAS benchmark dataset and two carbon traces, our trace-driven simulations demonstrate that CE-NAS achieves better carbon and search efficiency than the three baselines.

翻译：本文提出一种新颖的神经架构搜索方法，旨在降低模型设计过程中的能耗并提升碳效率。所提出的框架称为碳高效神经架构搜索（CE-NAS），由具备不同能耗需求的NAS评估算法、多目标优化器以及启发式GPU分配策略组成。CE-NAS根据当前碳排放量动态平衡节能采样与高能耗评估任务之间的关系。基于最新NAS基准数据集及两组碳排放轨迹数据的驱动仿真实验表明，CE-NAS在碳效率与搜索效率方面均优于三种基线方法。