Towards Sustainability of AI

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Streamline ML-Model Training Process new ​

Overview ​

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

Description ​

Previous research endeavors provide evidence that emissions from ML training can be significantly reduced when using servers within selected geographic regions at specific times (Dodge et al., 2022; Xu, 2022). Therefore, the DP “Streamline ML-Model Training Process” describes the optimization of the ML training setup to allow flexible training schedules and leverage renewable energy. An in-depth analysis of different techniques can be found in Xu (2022) and Radovanovic et al. (2023).

Sources ​

  • Dodge, J., Prewitt, T., Tachet des Combes, R., Odmark, E., Schwartz, R., Strubell, E., Luccioni, A. S., Smith, N. A., DeCario, N., & Buchanan, W. (2022). Measuring the Carbon Intensity of AI in Cloud Instances. Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, 1877–1894. https://doi.org/10.1145/3531146.3533234
  • Xu, T. (2022). These simple changes can make AI research much more energy efficient. MIT Technology Review. https://www.technologyreview.com/2022/07/06/1055458/ai-research-emissions-energy-efficient/
  • Radovanović, A., Koningstein, R., Schneider, I., Chen, B., Duarte, A., Roy, B., Xiao, D., Haridasan, M., Hung, P., Care, N., Talukdar, S., Mullen, E., Smith, K., Cottman, M., & Cirne, W. (2023). Carbon-Aware Computing for Datacenters. IEEE Transactions on Power Systems, 38(2), 1270–1280. https://doi.org/10.1109/TPWRS.2022.3173250