معرفی
Oliver Bringmann is a full Professor and head of the Chair of Embedded Systems at the University of Tübingen, Germany, and a member of the board of directors at the FZI Research Center for Information Technology. His research integrates embedded-system design, energy-efficient AI accelerators, dependable automotive perception, and medical AI for capsule endoscopy.
Education & Career
- Ph.D. in Computer Science, University of Tübingen, 2001
- Diploma in Computer Science, University of Karlsruhe (KIT)
- Head, Chair of Embedded Systems, University of Tübingen (since 2012)
- Deputy spokesperson & spokesperson, Dept. of Computer Science, University of Tübingen (2014-2022)
- Board of Directors, FZI Research Center for Information Technology
Research Interests
Bringmann’s group pioneers hardware/software co-design for ultra-low-power Edge-AI, developing RISC-V based accelerators, compiler-aware neural-architecture search, and real-time perception systems for autonomous driving and medical devices. Key topics include:
- Energy-efficient AI architectures (“Edge AI”) and custom accelerator generation
- Robust collective perception under adverse weather (LiDAR, camera, V2X fusion)
- Timing/power-predictable embedded software and system-on-chip design automation
- Hardware-assisted security and safety for automotive & IoT systems
- AI-driven capsule endoscopy localization and anomaly detection
Recent Publication Trends
His 2024-2025 articles reveal a strong shift toward robust multimodal perception for automated driving (snow, fog, collective LiDAR fusion) and Edge-AI medical devices (capsule endoscopy with multi-task CNNs). Core contributions span dataset generation (SCOPE, SnowyLane), safety metrics (LSM), and fast performance modeling for DNN accelerators.
Professional Service & Projects
- Executive/Steering Committees: IEEE/ACM DATE, CODES+ISSS, CASES, ITSS conferences
- EU CATRENE EDA roadmap chapter lead (Embedded Software & ESL-to-RTL)
- Principal investigator in Scale4Edge, OCEAN12, enerDAG and other national projects on energy-efficient sensorics and secure energy trading.
His group maintains extensive collaborations with automotive and semiconductor industry, focusing on dependable, energy-aware embedded intelligence.
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