Marc Neu, M.Sc.

Marc Neu, M.Sc.

  • Engesserstr. 5

    76131 Karlsruhe


Data Flow Processing for DAQ Systems

New advances in the field of telecommunications are leading to increasing demands on the bandwidth of digital signal processing. New standards are capable of transmitting sub-terabit data rates. Characterization of such systems requires processing, validation and storage of the generated measurement data. Stream processors play a key role in the development of data acquisition systems (DAQ systems). At ITIV, we aim to develop a framework for reconfigurable DAQ systems that will enable the specification of future transmission standards.

Implementation of GNNs on Hardware Accelerators

Graph Neural Networks (GNNs) extend conventional deep learning methods to graphical structures. Their generalized formulation opens up new possibilities in application areas such as image processing, monitoring or network analysis. Especially when implementing them in real-time applications, bandwidth and memory latency are significant bottlenecks. Therefore, the use of reprogrammable hardware platforms such as FPGAs is a central topic in current studies. At ITIV, we are trying to improve the usability of GNNs in embedded systems.

Data-driven design of trigger systems

Particle accelerators generate huge amounts of data during their experiments and therefore require so-called trigger systems. These systems implement filter mechanisms to distinguish between relevant and irrelevant detector events. Varying hyperparameters during physics experiments require automated training and reconfiguration of the firmware in the detector. Here at ITIV, we are investigating measures to adapt latency-optimized trigger systems to changing environmental variables.


Journal Articles
Real-Time Graph Building on FPGAs for Machine Learning Trigger Applications in Particle Physics
Neu, M.; Becker, J.; Dorwarth, P.; Ferber, T.; Reuter, L.; Stefkova, S.; Unger, K.
2024. Computing and Software for Big Science, 8 (1), Artkl.Nr.: 8. doi:10.1007/s41781-024-00117-0
Book Chapters
A Convolution Neural Network Based Displaced Vertex Trigger for the Belle II Experiment
Unger, K.; Becker, J.; Kiesling, C.; Ma, Y.; Meggendorfer, F.; Neu, M.; Schmidt, E.; Zweigart, U.
2023. Applied Reconfigurable Computing. Architectures, Tools, and Applications – 19th International Symposium, ARC 2023, Cottbus, Germany, September 27–29, 2023, Proceedings. Ed.: F. Palumbo, 173–184, Springer Nature Switzerland. doi:10.1007/978-3-031-42921-7_12
Journal Articles
Data-driven design of the Belle II track segment finder
Unger, K. L.; Neu, M.; Becker, J.; Schmidt, E.; Kiesling, C.; Meggendorfer, F.; Skambraks, S.
2023. Journal of Instrumentation, 18 (2), Art.-Nr.: C02001. doi:10.1088/1748-0221/18/02/C02001
Conference Papers
A Scalable and Cost-Efficient Antenna Testbed Using FPGA-Server Compound Structures for Prototyping 6G Applications
Neu, M.; Karle, C.; Nuss, B.; Groeschel, P.; Becker, J.
2023. 2023 19th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT), 171–178, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/DCOSS-IoT58021.2023.00039
A Unified SoC Lab Course: Combined Teaching of Mixed Signal Aspects, System Integration, Software Development and Documentation
Pfau, J.; Leys, R.; Neu, M.; Serdyuk, A.; Peric, I.; Becker, J.
2023. 2023 IEEE International Symposium on Circuits and Systems (ISCAS), 5 S., Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ISCAS46773.2023.10181679
ReLoDAQ: Resource-Efficient, Low-Overhead 200 Gbits −1 Data Acquisition System for 6G Prototyping
Karle, C.; Neu, M.; Pfau, J.; Sperling, J.; Becker, J.
2023. 2023 IEEE 31st Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM), 08-11 May 2023, Marina Del Rey, CA, USA, 209, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/FCCM57271.2023.00037