EC-NAS: Energy Consumption Aware Tabular Benchmarks for Neural Architecture Search

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.