Energy Efficiency Considerations for Popular AI Benchmarks

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.

Citation information

Fischer, Raphael; Jakobs, Matthias; Morik, Katharina: Energy Efficiency Considerations for Popular AI Benchmarks, arXiv, 2023, https://arxiv.org/abs/2304.08359, Fischer.etal.2023b,

Associated Lamarr Researchers

Portrait of Raphael Fischer.

Raphael Fischer

Author to the profile
Portrait of Matthias Jakobs.

Matthias Jakobs

Scientist to the profile
lamarr institute person Morik Katharina e1663924705259 - Lamarr Institute for Machine Learning (ML) and Artificial Intelligence (AI)

Prof. Dr. Katharina Morik

Founding Director to the profile