Bears: Building Energy-Aware Reconfigurable Systems

Herzog B, Reif S, Hügel F, Schröder-Preikschat W, Hönig T (2022)

Publication Type: Conference contribution

Publication year: 2022

Publisher: IEEE Computer Society

Book Volume: 2022-November

Conference Proceedings Title: Brazilian Symposium on Computing System Engineering, SBESC

Event location: Fortaleza BR

ISBN: 9781665474252

DOI: 10.1109/SBESC56799.2022.9964629


Energy efficiency has developed to one of the most important non-functional system properties. One keystone to building an energy-efficient system is the right system configuration, which is tailored to the currently running application and hardware. Finding such a right system configuration manually, however, is a complex and often unfeasible task due to the vast configuration space on the one side and the required hardware and application knowledge on the other side. This paper presents and refines an approach to automatically identify and select energy-efficient configurations in re-configurable systems. The approach relies on different machine-learning techniques and achieves energy efficiency improvements of up to 10.8 % out of 13.3 % by automatically adapting the system configuration on a Linux system. Additionally, we analyse the application knowledge required for selecting the configuration and make a proposal how to generate sufficient training data.

Authors with CRIS profile

Involved external institutions

How to cite


Herzog, B., Reif, S., Hügel, F., Schröder-Preikschat, W., & Hönig, T. (2022). Bears: Building Energy-Aware Reconfigurable Systems. In Brazilian Symposium on Computing System Engineering, SBESC. Fortaleza, BR: IEEE Computer Society.


Herzog, Benedict, et al. "Bears: Building Energy-Aware Reconfigurable Systems." Proceedings of the 12th Brazilian Symposium on Computing Systems Engineering, SBESC 2022, Fortaleza IEEE Computer Society, 2022.

BibTeX: Download