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Joint European Conference on Machine Learning and Knowledge Discovery in Databases

ECML PKDD 2022: Machine Learning and Principles and Practice of Knowledge Discovery in Databases pp 39–54Cite as

A Unified Framework for Assessing Energy Efficiency of Machine Learning

Part of the Communications in Computer and Information Science book series (CCIS,volume 1752)

Abstract

State-of-the-art machine learning (ML) systems show exceptional qualitative performance, but can also have a negative impact on society. With regard to global climate change, the question of resource consumption and sustainability becomes more and more urgent. The enormous energy footprint of single ML applications and experiments was recently investigated. However, environment-aware users require a unified framework to assess, compare, and report the efficiency and performance trade-off of different methods and models. In this work we propose novel efficiency aggregation, indexing, and rating procedures for ML applications. To this end, we devise a set of metrics that allow for a holistic view, taking both task type, abstract model, software, and hardware into account. As a result, ML systems become comparable even across different execution environments. Inspired by the EU’s energy label system, we also introduce a concept for visually communicating efficiency information to the public in a comprehensible way. We apply our methods to over 20 SOTA models on a range of hardware architectures, giving an overview of the modern ML efficiency landscape.

Keywords

  • Energy efficiency
  • Sustainability
  • Resource-aware ML
  • Green AI
  • Trustworthy AI

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Notes

  1. 1.

    www.github.com/raphischer/imagenet-energy-efficiency.

  2. 2.

    www.pypi.org/project/pynvml/.

  3. 3.

    www.pypi.org/project/pyRAPL/.

  4. 4.

    www.intel.com/content/www/us/en/developer/articles/tool/power-gadget.html.

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Acknowledgement

This research has been funded by the Federal Ministry of Education and Research of Germany and the state of North-Rhine Westphalia as part of the Lamarr-Institute for Machine Learning and Artificial Intelligence, LAMARR22B.

Author information

Authors and Affiliations

  1. AI Group, TU Dortmund University, 44227, Dortmund, Germany

    Raphael Fischer, Matthias Jakobs, Sascha Mücke & Katharina Morik

Authors
  1. Raphael Fischer
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  2. Matthias Jakobs
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  3. Sascha Mücke
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  4. Katharina Morik
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Corresponding author

Correspondence to Raphael Fischer .

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Editors and Affiliations

  1. University of Sydney, Sydney, Australia

    Irena Koprinska

  2. University of Bari Aldo Moro, Bari, Italy

    Paolo Mignone

  3. University of Pisa, Pisa, Italy

    Riccardo Guidotti

  4. Warsaw University of Technology, Warsaw, Poland

    Szymon Jaroszewicz

  5. Heidelberg University, Heidelberg, Germany

    Holger Fröning

  6. UniCredit, Rome, Italy

    Francesco Gullo

  7. University of Lisbon, Lisbon, Portugal

    Pedro M. Ferreira

  8. Roche, Basel, Switzerland

    Damian Roqueiro

  9. Barcelona Supercomputing Center, Barcelona, Spain

    Gaia Ceddia

  10. Halmstad University, Halmstad, Sweden

    Slawomir Nowaczyk

  11. University of Porto, Porto, Portugal

    João Gama

  12. University of Porto, Porto, Portugal

    Rita Ribeiro

  13. UPC BarcelonaTech, Barcelona, Spain

    Ricard Gavaldà

  14. University of Naples Federico II, Naples, Italy

    Elio Masciari

  15. University of North Carolina, Charlotte, USA

    Zbigniew Ras

  16. ICAR-CNR, Rende, Italy

    Ettore Ritacco

  17. University of Pisa, Pisa, Italy

    Francesca Naretto

  18. Aalen University of Applied Sciences, Aalen, Germany

    Andreas Theissler

  19. Warsaw University of Technology, Warszaw, Poland

    Przemyslaw Biecek

  20. KU Leuven, Leuven, Belgium

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  21. University of Duisburg-Essen, Essen, Germany

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  22. Graz University of Technology, Graz, Austria

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  23. AMD, Dublin, Ireland

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  24. UniCredit, Rome, Italy

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  25. UniCredit, Milan, Italy

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  26. National Agency for New Technologies, Rome, Italy

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  27. Unicredit, Rome, Italy

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  28. University of Bari Aldo Moro, Bari, Italy

    Annalisa Appice

  29. University of Bari Aldo Moro, Bari, Italy

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  30. University of Lisbon, Lisbon, Portugal

    Ibéria Medeiros

  31. University of Lisbon, Lisbon, Portugal

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  32. Northwestern University, Chicago, USA

    Lee Cooper

  33. Roche, Basel, Switzerland

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  34. University of Lausanne, Lausanne, Switzerland

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  35. Novartis, Basel, Switzerland

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  37. Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy

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  38. Politecnico di Milano, Milan, Italy

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  39. Politecnico di Milano, Milan, Italy

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  40. University of Waikato, Hamilton, New Zealand

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  41. Halmstad University, Halmstad, Sweden

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Fischer, R., Jakobs, M., Mücke, S., Morik, K. (2023). A Unified Framework for Assessing Energy Efficiency of Machine Learning. In: Koprinska, I., et al. Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2022. Communications in Computer and Information Science, vol 1752. Springer, Cham. https://doi.org/10.1007/978-3-031-23618-1_3

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  • DOI: https://doi.org/10.1007/978-3-031-23618-1_3

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