18.03.2025
©TUM
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
Related
29.10.2026
Teaching Language Models to Express Uncertainty
An ICLR 2026 paper shows how reinforcement learning improves confidence estimates from language models.
27.10.2026
Runner-Up Prize at AutoML 2026 for Tabular-DC-Bench
Tabular-DC-Bench, by MCML Junior Member Mamdouh Aljoud and MCML Director Thomas Seidl, among others, won a runner-up prize at AutoML 2026.
27.10.2026
Welcoming Ewa Szczurek to MCML
Ewa Szczurek joins MCML as Professor of AI in Life Sciences at LMU, focusing on probabilistic and generative models for medicine.