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.

  • Published in:
    AI for Energy Innovation Workshop at AAAI Conference on Artificial Intelligence
  • Type:
    Inproceedings
  • Authors:
    Fischer, Raphael; Jakobs, Matthias; Morik, Katharina
  • Year:
    2023

Citation information

Fischer, Raphael; Jakobs, Matthias; Morik, Katharina: Energy Efficiency Considerations for Popular AI Benchmarks, AI for Energy Innovation Workshop at AAAI Conference on Artificial Intelligence, 2023, https://arxiv.org/abs/2304.08359, Fischer.etal.2023b,

Associated Lamarr Researchers

lamarr institute person Fischer Raphael - Lamarr Institute for Machine Learning (ML) and Artificial Intelligence (AI)

Raphael Fischer

Author to the profile
lamarr institute person Jakobs Matthias - Lamarr Institute for Machine Learning (ML) and Artificial Intelligence (AI)

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