by Tove Kvarnström | Feb 28, 2023
PI: Amir Aminifar (LU); co-PIs: Christoph Kessler (LiU), Petru Eles (LiU) The new generation of artificial intelligence (AI) and machine learning (ML) techniques have recently been shown to require non-negligible energy for both training and inference. FL4IoT will...
by Tove Kvarnström | Feb 28, 2023
PI: Elina Rönnberg (LiU); co-PI: Susanna F. de Rezende (LU) Improved methods for solving discrete optimisation problems have a great potential to contribute to sustainability and energy efficiency, as well as to trustworthiness of systems. The goal of this project is...
by Tove Kvarnström | Feb 28, 2023
PI: Andreas Kerren (LiU); co-PI: Richard Pates (LU) Multilayer networks are a relatively new way to model complex real-world systems that demand novel and efficient solutions for their analysis. Especially when regarding large and heterogeneous data typically used in...
by Tove Kvarnström | Feb 28, 2023
PI: Oscar Gustafsson (LiU); co-PI: Joachim Rodrigues (LU) Trading energy for accuracy is a promising approach to reduce the computing energy dissipation. Especially, for classes of applications with inherent resiliency, including AI/ML, significant reductions can be...
by Tove Kvarnström | Feb 28, 2023
PI: Daniel Axehill (LiU); co-PI: Björn Olofsson (LU) The objective with the proposed research is to significantly extend the recent work by the PI’s former PhD students Oskar Ljungqvist and Kristoffer Bergman in the area of optimization-based motion planning and...
