Energy Efficiency Considerations for Popular AI Benchmarks
Not on the map. Why?
Abstract (arXiv)
Advances in artificial intelligence need to become more resource-aware and sustainable. This requires clear assessment and reporting of energy efficiency trade-offs, like sacrificing fast running time for higher predictive performance. While first methods for investigating efficiency have been proposed, we still lack comprehensive results for popular methods and data sets. In this work, we attempt to fill this information gap by providing empiric insights for popular AI benchmarks, with a total of 100 experiments. Our findings are evidence of how different data sets all have their own efficiency landscape, and show that methods can be more or less likely to act efficiently.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 8 of its 13 references that are in the map. First posted on arXiv 17 Apr 2023.
Builds on
8 of 13 references.
References · 8
- 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
- 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,845
- 2019Green AIcited by 627
- 2021Carbon Emissions and Large Neural Network Trainingcited by 567
- 2014OpenML: networked science in machine learningcited by 526
- 2020Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learningcited by 249
- 2020Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claimscited by 165
- 2021Yes We Care! -- Certification for Machine Learning Methods through the Care Label Frameworkcited by 5
Built on by
Citing papers per year: 2024: 1.
1 paper.