A
Andreas Bernauer
Researcher at University of Tübingen
Publications - 19
Citations - 160
Andreas Bernauer is an academic researcher from University of Tübingen. The author has contributed to research in topics: Learning classifier system & System on a chip. The author has an hindex of 7, co-authored 19 publications receiving 154 citations.
Papers
More filters
Proceedings ArticleDOI
Organic Computing at the System on Chip Level
A. Bouajila,J. Zeppenfeld,Walter Stechele,Andreas Herkersdorf,Andreas Bernauer,Oliver Bringmann,Wolfgang Rosenstiel +6 more
TL;DR: This paper presents an organic computing inspired SoC architecture which applies self-organization and self-calibration concepts to build reliable SoCs with lower overheads and a broader fault coverage than classical fault-tolerance techniques.
Proceedings ArticleDOI
Generic Self-Adaptation to Reduce Design Effort for System-on-Chip
TL;DR: The proposed generic self-adaptation method helps to improve the design process by allowing design reuse, providing generic applicability, and offering a uniform design process for various self- Adaptation tasks.
An Architecture for Runtime Evaluation of SoC Reliability
Andreas Bernauer,Oliver Bringmann,Wolfgang Rosenstiel,Abdelmajid Bouajila,Walter Stechele,Andreas Herkersdorf +5 more
TL;DR: This paper presents an architecture to evaluate the reliability of a systemon-chip (SoC) during its runtime that also accounts for the system’s redundancy and proposes to integrate an autonomic layer into the SoC to detect the chip's current condition and instruct appropriate countermeasures.
Book ChapterDOI
Combining Software and Hardware LCS for Lightweight On-Chip Learning
Andreas Bernauer,Johannes Zeppenfeld,Oliver Bringmann,Andreas Herkersdorf,Wolfgang Rosenstiel +4 more
TL;DR: A novel two-stage method is presented to realise a lightweight but very capable hardware implementation of a Learning Classifier System for on-chip learning.
Applying ASoC to Multi-core Applications for Workload Management
Johannes Zeppenfeld,Abdelmajid Bouajila,Walter Stechele,Andreas Herkersdorf,Andreas Bernauer,Oliver Bringmann,Wolfgang Rosenstiel +6 more
TL;DR: It is shown that Learning Classifier Tables, a simplified XCS-based reinforcement learning technique optimised for a low-overhead hardware implementation and integration, achieve nearly optimal results for task-level dynamic workload balancing during run time for a standard networking application.