Energy consumption from selecting, training and deploying deep learning models has continued to increase over the past few years. Our goal in this work is to support the design of energy-efficient deep learning models that are easier to train with lower compute resources, practical to deploy in real-world edge/mobile computing settings and environmentally sustainable. Tabular benchmarks for neural architecture search (NAS) allow the evaluation of NAS strategies at lower computational cost by providing pre-computed performance statistics. In this work, we suggest including energy efficiency as an additional performance criterion to NAS and present an updated tabular benchmark by including information on energy consumption and carbon footprint for different architectures. The benchmark called EC-NAS is made available open-source to support energy consumption-aware NAS research. EC-NAS also includes a surrogate model for predicting energy consumption, and helps us reduce the overall energy cost of creating this dataset. We demonstrate the usefulness of EC-NAS by applying multi-objective optimisation algorithms that reveal the trade-off between energy consumption and accuracy, showing that it is possible to discover energy-efficient architectures with little to no loss in performance.

翻译：近年来，深度学习模型在选取、训练和部署过程中产生的能耗持续增长。本工作旨在支持设计更易在低计算资源下训练的、可在真实边缘/移动计算场景中实际部署的、且环境可持续的节能深度学习模型。神经网络架构搜索（NAS）的表格基准通过提供预计算性能统计量，使得NAS策略评估的计算成本更低。本文建议将能效作为附加性能指标纳入NAS，并通过补充不同架构的能耗与碳足迹信息，提出更新的表格基准。该基准名为EC-NAS，以开源形式提供，旨在支撑能耗感知型NAS研究。EC-NAS还包含用于预测能耗的替代模型，有助于降低该数据集创建过程中的总能耗成本。通过应用多目标优化算法，我们揭示了能耗与准确率之间的权衡关系，论证了EC-NAS的实用性——研究表明，在性能几乎不受损的情况下，仍可发现节能型架构。