  Energy consumption from the selection, training, and deployment of deep
learning models has seen a significant uptick recently. This work aims to
facilitate the design of energy-efficient deep learning models that require
less computational resources and prioritize environmental sustainability by
focusing on the energy consumption. Neural architecture search (NAS) benefits
from tabular benchmarks, which evaluate NAS strategies cost-effectively through
precomputed performance statistics. We advocate for including energy efficiency
as an additional performance criterion in NAS. To this end, we introduce an
enhanced tabular benchmark encompassing data on energy consumption for varied
architectures. The benchmark, designated as EC-NAS, has been made available in
an open-source format to advance research in energy-conscious NAS. EC-NAS
incorporates a surrogate model to predict energy consumption, aiding in
diminishing the energy expenditure of the dataset creation. Our findings
emphasize the potential of EC-NAS by leveraging multi-objective optimization
algorithms, revealing a balance between energy usage and accuracy. This
suggests the feasibility of identifying energy-lean architectures with little
or no compromise in performance.
