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.
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