Research interests

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.

Teaching

Student works to be assigned
title	type
Automated and split Binding of Software Components to Execution Units on the Basis of Safety and Security Premises	Masterarbeit
Automated Context Adaption of Iterative Software in Constraint Execution Environments	Masterarbeit
Automated AI-based Placement of Early Exits in Deep Neural Networks	Masterarbeit
Safety and Security for AI Accelerators - Tracing Layer Activations	Bachelorarbeit
Parallel result validation of AI accelerators using most neuron activation monitor	Masterarbeit

HiWi Positions
Title
Hardware Implementation of Early Exits in Hardware Accelerators
Hardware Implementation of Anomaly Detection Strategies in Hardware Accelerators

Publications

2024

Conference Papers

EMDRIVE Architecture: Embedded Distributed Computing and Diagnostics from Sensor to Edge
Schmidt, P.; Topko, I.; Stammler, M.; Harbaum, T.; Becker, J.; Berner, R.; Ahmed, O.; Jagielski, J.; Seidler, T.; Abel, M.; Kreutzer, M.; Kirschner, M.; Betancourt, V. P.; Sehm, R.; Groth, L.; Neskovic, A.; Meyer, R.; Mulhem, S.; Berekovic, M.; Probst, M.; Brosch, M.; Sigl, G.; Wild, T.; Ernst, M.; Herkersdorf, A.; Aigner, F.; Hommes, S.; Lauer, S.; Seidler, M.; Raste, T.; Bozic, G. S.; Ceberio, I. I.; Hassan, M.; Mayer, A.
2024. 27th Design, Automation and Test in Europe Conference and Exhibition (DATE 2024), 6 S., Institute of Electrical and Electronics Engineers (IEEE). doi:10.23919/DATE58400.2024.10546796

RVVe: A Minimal RISC-V Vector Processor for Embedded AI Acceleration
Schmidt, P.; Pfau, J.; Hotfilter, T.; Stammler, M.; Harbaum, T.; Becker, J.
2024. 2024 IEEE 37th International System-on-Chip Conference (SOCC), Dresden, 16th-19th September 2024, 1–6, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/SOCC62300.2024.10737723

Mitigating Masking in Automotive Communication Systems: Modeling and Hardware Generation
Stammler, M.; Hamann, M.; Harbaum, T.; Becker, J.
2024. 2023 26th Euromicro Conference on Digital System Design (DSD), Golem, 6th - 8th September 2023, 561 – 568, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/DSD60849.2023.00083

Context-Aware Layer Scheduling for Seamless Neural Network Inference in Cloud-Edge Systems
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

2023

Journal Articles

EFFECT: An End-to-End Framework for Evaluating Strategies for Parallel AI Anomaly Detection
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