From Robustness to Worst-Case Timing Analysis: Reusing Neural Network Abstract Interpretation with DeepWCET

Häberlein T, Penk D, Riedelbauch D, Stilkerich I, Wägemann P (2026)

Publication Type: Conference contribution, Conference Contribution

Publication year: 2026

Conference Proceedings Title: Proceedings of the 34th International Conference on Real-Time Networks and Systems (RTNS '26)

Event location: Toulouse, France

URI: https://sys.cs.fau.de/publications/2026/haeberlein_26_rtns.pdf

Open Access Link: https://sys.cs.fau.de/publications/2026/haeberlein_26_rtns.pdf

Authors with CRIS profile

Tobias Häberlein Lehrstuhl für Informatik 4 (Systemsoftware) Peter Wägemann Lehrstuhl für Informatik 4 (Systemsoftware)

Related research project(s)

Abstract Interpretation for Embedded AI Code Safety (AIMBOS) Jan. 1, 2025 - Dec. 31, 2027 Whole-System Optimality Analysis and Tailoring of Worst-Case–Constrained Applications (Watwa) Nov. 1, 2022 - Nov. 1, 2025

Involved external institutions

Schaeffler Technologies AG & Co. KG DE Germany (DE)

How to cite

APA:

Häberlein, T., Penk, D., Riedelbauch, D., Stilkerich, I., & Wägemann, P. (2026). From Robustness to Worst-Case Timing Analysis: Reusing Neural Network Abstract Interpretation with DeepWCET. In Proceedings of the 34th International Conference on Real-Time Networks and Systems (RTNS '26). Toulouse, France.

MLA:

Häberlein, Tobias, et al. "From Robustness to Worst-Case Timing Analysis: Reusing Neural Network Abstract Interpretation with DeepWCET." Proceedings of the 34th International Conference on Real-Time Networks and Systems (RTNS 2026), Toulouse, France 2026.

BibTeX: Download