Masterarbeiten zu Software
| Thema: | GPU-accelerated track reconstruction using ACTS and Graph Neural Networks at Belle II and the FCC-ee |
| Zusammenfassung: | Modern collider experiments like Belle II and the proposed FCC-ee generate large amounts of tracking data. Graph Neural Networks (GNNs) are increasingly used to find track candidates from raw hits. In this project, you will take the output of a GNN-based track finder and integrate it into ACTS (A Common Tracking Software), a modern C++ toolkit for track fitting. The focus is on implementing this connection and optimizing it for execution on GPUs. You will work in C++ and use accelerator frameworks like CUDA or SYCL to build a fast and scalable solution. The setup should be modular and suitable for both current (Belle II) and future (FCC-ee) detectors. For an example of GNN-based track finding, see the CAT project at Belle II. |
| Sie lernen kennen: | ACTS track reconstruction, GPU programming in C++ (CUDA/SYCL), integration of machine learning output, performance tuning and benchmarking, software development in particle physics |
| Referent: | Prof. Dr. Torben Ferber |
| Ansprechpartner: | Dr. Giacomo De Pietro |
| Letzte Änderung: | 05.04.2025 |
| Thema: | FPGA-accelerated tracking using AMD Alveo V80 accelerator cards |
| Zusammenfassung: | Real-time tracking in high-rate environments like Belle II or FCC-ee requires serious compute power, but maybe GPUs aren't always the answer. In this project, you'll explore an alternative: running core parts of the track reconstruction pipeline on AMD Alveo V80 FPGA cards, built for low-latency acceleration in data centers. Using high-level synthesis (HLS) and streaming I/O, you’ll implement modules such as hit filtering and candidate selection on the FPGA, in close collaboration with FPGA experts at ITIV. |
| Sie lernen kennen: | FPGA programming with HLS (e.g. Vitis), low-latency data processing, particle tracking logic, hardware/software co-design, performance tuning, resource optimization |
| Referent: | Prof. Dr. Torben Ferber |
| Ansprechpartner: | Marc Neu |
| Letzte Änderung: | 05.04.2025 |
| Thema: | Next generation AI for the upgrade of the Belle II track trigger |
| Zusammenfassung: | The Belle II experiment relies on its Central Drift Chamber (CDC) for precise charged particle tracking and momentum measurement. As the experiment prepares for future upgrades and increased luminosity, improving the CDC track reconstruction algorithms becomes a crucial challenge. The candidate will work within the interdisciplinary CDC-ML team at KIT ETP and ITIV to develop real-time machine learning-based tracking methods. Special attention will be given to handling background noise and missing hits, as well as optimizing inference on cutting-edge real-time hardware, such as AMD Xilinx Versal AI Edge Series Gen 2 platforms. The CDC-ML team has access to dedicated hardware at KIT for testing machine learning models on real-time architectures, ensuring that proposed solutions can be validated under experimental conditions. Close collaboration with electrical engineering students at ITIV will be necessary to co-design efficient hardware-software interfaces for real-time inference. |
| Sie lernen kennen: | Python programming, machine learning, track reconstruction, algorithm optimization |
| Referent: | Prof. Dr. Torben Ferber |
| Ansprechpartner: | Prof. Dr. Torben Ferber |
| Letzte Änderung: | 05.04.2025 |