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

Related

Link to Teaching Language Models to Express Uncertainty

29.10.2026

Teaching Language Models to Express Uncertainty

An ICLR 2026 paper shows how reinforcement learning improves confidence estimates from language models.

Read more
Link to Runner-Up Prize at AutoML 2026 for Tabular-DC-Bench

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.

Read more
Link to Welcoming Ewa Szczurek to MCML

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.

Read more
Link to Best Paper Award at AIES 2026 for Work on Algorithmic Contestability

26.10.2026

Best Paper Award at AIES 2026 for Work on Algorithmic Contestability

MCML Junior Member Timo Freiesleben received the Best Paper Award at AIES 2026 for his work on algorithmic contestability.

Read more
Tiny logo
Link to MCML at EMNLP 2026

23.10.2026

MCML at EMNLP 2026

MCML researchers are represented with 47 papers at EMNLP 2026 (31 Main, 11 Findings, and 5 Workshops).

Read more
Back to Top