18.03.2025

Teaser image to New Method Significantly Reduces AI Energy Consumption

New Method Significantly Reduces AI Energy Consumption

TUM News

Researchers at the Technical University of Munich have developed an innovative method that drastically lowers the energy consumption of artificial intelligence systems. The approach optimizes computational efficiency, making AI applications more sustainable and cost-effective.

Our Associate Felix Dietrich emphasized the importance of energy-efficient AI, highlighting its potential to reduce environmental impact while maintaining high-performance capabilities.

«Our method makes it possible to determine the required parameters with minimal computing power. This can make the training of neural networks much faster and, as a result, more energy efficient.»


Felix Dietrich

MCML Associate

#research #dietrich
Subscribe to RSS News feed

Related

Link to Rethinking AI in Public Institutions - Balancing Prediction and Capacity

09.10.2025

Rethinking AI in Public Institutions - Balancing Prediction and Capacity

Unai Fischer Abaigar explores how AI can make public decisions fairer, smarter, and more effective.

Link to MCML-LAMARR Workshop at University of Bonn

08.10.2025

MCML-LAMARR Workshop at University of Bonn

MCML and Lamarr researchers met in Bonn to exchange ideas on NLP, LLM finetuning, and AI ethics.

Link to Three MCML Members Win Best Paper Award at AutoML 2025

08.10.2025

Three MCML Members Win Best Paper Award at AutoML 2025

Former MCML TBF Matthias Feurer and Director Bernd Bischl’s paper on overtuning won Best Paper at AutoML 2025, offering insights for robust HPO.

Link to Machine Learning for Climate Action - with researcher Kerstin Forster

29.09.2025

Machine Learning for Climate Action - With Researcher Kerstin Forster

Kerstin Forster researches how AI can cut emissions, boost renewable energy, and drive corporate sustainability.

Link to Making Machine Learning More Accessible with AutoML

26.09.2025

Making Machine Learning More Accessible With AutoML

Matthias Feurer discusses AutoML, hyperparameter optimization, OpenML, and making machine learning more accessible and efficient for researchers.

Back to Top