Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks
Abstract
Neural Architecture Search (NAS) accelerates progress in deep learning through systematic refinement of model architectures. The downside is increasingly large energy consumption during the search process. Surrogate-based benchmarking mitigates the cost of full training by querying a pre-trained surrogate to obtain an estimate for the quality of the model. Specifically, energy-aware benchmarking aims to make it possible for NAS to favourably trade off model energy consumption against accuracy. Towards this end, we propose three design principles for such energy-aware benchmarks: (i) reliable power measurements, (ii) a wide range of GPU usage, and (iii) holistic cost reporting. We analyse EA-HAS-Bench based on these principles and find that the choice of GPU measurement API has a large impact on the quality of results. Using the Nvidia System Management Interface (SMI) on top of its underlying library influences the sampling rate during the initial data collection, returning faulty low-power estimations. This results in poor correlation with accurate measurements obtained from an external power meter. With this study, we bring to attention several key considerations when performing energy-aware surrogate-based benchmarking and derive first guidelines that can help design novel benchmarks. We show a narrow usage range of the four GPUs attached to our device, ranging from 146 W to 305 W in a single-GPU setting, and narrowing down even further when using all four GPUs. To improve holistic energy reporting, we propose calibration experiments over assumptions made in popular tools, such as Code Carbon, thus achieving reductions in the maximum inaccuracy from 10.3 % to 8.9 % without and to 6.6 % with prior estimation of the expected load on the device.
Details
- Organisation(s)
-
Institute of Artificial Intelligence
Machine Learning Section
- External Organisation(s)
-
Leiden University
RWTH Aachen University
Technische Universität Darmstadt
- Type
- Conference contribution
- Pages
- 50-59
- No. of pages
- 10
- Publication date
- 2025
- Publication status
- Published
- Peer reviewed
- Yes
- ASJC Scopus subject areas
- Information Systems, Hardware and Architecture, Information Systems and Management, Safety, Risk, Reliability and Quality, Computer Networks and Communications, Artificial Intelligence
- Sustainable Development Goals
- SDG 7 - Affordable and Clean Energy
- Electronic version(s)
-
https://doi.org/10.1109/CCGridW65158.2025.00017 (Access:
Closed
)
https://doi.org/10.48550/arXiv.2505.15631 (Access: Open )
