Jakob Matthias Stammler, M.Sc.
- Wissenschaftlicher Mitarbeiter
- Group: Prof. Becker
- Room:
CS 30.10 - Phone: +49 721 608-41322
- stammler ∂does-not-exist.kit edu
Engesserstr. 5
76131 Karlsruhe
Monitoring of safety security-relevant incidents
Developing autonomous acting vehicles requires a working and reliable collaboration between functions, (sub-)systems, and services. These collaborations are not limited to the vehicle itself but continue well into the development cycle. A continuously changing landscape of cyber security risks and the fulfillment of functionality (SOTIF) require dynamic monitoring of a wide range of vehicle sensor data. Deciding which data should be monitored and saved, how this can be achieved and the integration into the next development cycle is an open question that gets many different answers frequently.
Making stochastic components certifiable
Machine learning components are becoming increasingly widespread in a wide range of contexts and achieve remarkable performance. Because of their stochastic nature, their use isn’t deterministic and they often act like a “black box”, which makes certification difficult. Creating frameworks and models to simulate or predict their behavior could yield a more controllable and formal approach of combining the high performance of ML components with the predictability of proven conventional components. These models shouldn’t be limited to ML components but are usable in different scenarios.
Graceful Degradation strategies
The failing of a non-critical subsystem shouldn’t result in a complete failure of a whole mission. Often redundant hardware components are used to reduce the mean time between failure. This at least doubles the installed hardware and makes the whole system more expensive. If the redundant hardware isn’t needed it is idle and takes up unnecessary space and power. To mitigate this, non-critical tasks could be executed on these systems, that might be canceled on demand. Creating sophisticated models and frameworks that predict the behavior of these systems is necessary for predictability and legal certification.
| title | type | Date |
|---|---|---|
| Safety and Security for AI Accelerators - Tracing Layer Activations | Bachelorarbeit | ab 03 / 2023 |
| Automated AI-based Placement of Early Exits in Deep Neural Networks | Masterarbeit | ab 02 / 2024 |
| Automated Context Adaption of Iterative Software in Constraint Execution Environments | Masterarbeit | ab 02 / 2024 |
| Parallel result validation of AI accelerators using most neuron activation monitor | Masterarbeit | ab 03 / 2023 |
| Automated and split Binding of Software Components to Execution Units on the Basis of Safety and Security Premises | Masterarbeit | ab 02 / 2024 |
| Title | Starting Date |
|---|---|
| Hardware Implementation of Early Exits in Hardware Accelerators | ab 02 / 2024 |
| Hardware Implementation of Anomaly Detection Strategies in Hardware Accelerators | ab 02 / 2024 |
Publications
Stammler, M.; Sidorenko, V.; Kreß, F.; Schmidt, P.; Becker, J.
2024. 2023 IEEE 16th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC), Singapur, 18th-21st December 2023, 97–104, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/MCSoC60832.2023.00022
Stammler, M.; Höfer, J.; Kraus, D.; Schmidt, P.; Hotfilter, T.; Harbaum, T.; Becker, J.
2023. Procedia Computer Science, 222, 499 – 508. doi:10.1016/j.procs.2023.08.188
Stammler, M.; Hamann, M.; Becker, J.
2023. 2023 12th Mediterranean Conference on Embedded Computing (MECO), 1–6, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/MECO58584.2023.10154914