Towards Sustainability of AI

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Preselect Energy-Efficient ML Models new ​

Overview ​

Sustainability DimensionEcological
ML Development PhaseModeling and Training
ML Development StakeholdersML Development, Software Development

Description ​

The DP “Preselect Energy-Efficient ML Models” focuses on selecting ML models from the perspective of an ML model’s lifetime carbon footprint in relation to its performance (Henderson et al., 2022; Strubell et al., 2019). It is advised to consider simpler ML models such as boosted trees instead of deep neural networks, pre-trained ML models, or transfer learning for ML models (Henderson et al., 2022). Estimates like floating-point operations of an ML model can guide the decision process (Schwartz et al., 2020).

Sources ​

  • Henderson, P., Hu, J., Romoff, J., Brunskill, E., Jurafsky, D., & Pineau, J. (2022). Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning. The Journal of Machine Learning Research, 1(2). https://doi.org/10.48550/arXiv.2002.05651
  • Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and Policy Considerations for Deep Learning in NLP. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 3645–3650. https://doi.org/10.18653/v1/P19-1355
  • Schwartz, R., Dodge, J., Smith, N. A., & Etzioni, O. (2020). Green AI. Communications of the ACM, 63(12), 54–63. https://doi.org/10.1145/3381831