Below is a list of open, ongoing, and completed projects/theses. If you are intested in an open project/thesis, or you alreay have a topic and looking for a supervisor, please contact me.

Open Projects/Theses

• Optimizing Dynamic Programming Algorithms for Hopper Architecture (B.Sc./M.Sc.)
• Benchmarking Embedded GPUs for Onboard AI Space Applications (B.Sc./M.Sc.)
• Developing Comprehensive Error Injection Models for Embedded GPUs for Space Applications (B.Sc./M.Sc.)
• Quantifying Performance Overhead of Embedded GPUs for Space Qualification (B.Sc./M.Sc.)
• A Quantitative Study of Space-Grade Embedded GPUs vs Off-The-Shelf Embedded GPUs for Onboard AI Processing (B.Sc./M.Sc.)
• A Comparative Study of LSTM Versus Transformer for Sequence Modeling Problems in GPUs (B.Sc./M.Sc.)
• A Generic Framework for Memory Access Granularity Aware Lossless Compression for Many-core Processors (B.Sc./M.Sc.)
• Evaluating Scalability of SYCL Applications Across HPC Clusters (B.Sc./M.Sc.)

Completed Projects/Theses

• Performance Modeling of NVIDIA H100’s SM-to-SM Network (Tobias Marschner)
• Advanced GPU Performance Modeling with Interval Analysis (Alexander Brosig)
• Gap Analysis for Integrating Personal Healthcare Devices into a Smart Home Network Using the Matter Protocol (Prajwal Kumar)
• Study and Evaluation of the Usage of Artifical Intelligence in Modern SoC Validation Process (Kowshic Ahmed Akash)
• Memory Access Granularity Aware Lossless Compression for GPUs (M. Renz)
• An Efficient Lightweight Framework for Porting Vision Algos on Embedded SoC (A. Ashish)
• SystemC Modeling of a Memory Paging System for Secure Elements in SoC (M. Yuan)
• Power Modeling of Mobile GPUs using Deep Learning (M. Neu)
• Quantitative Cache Line Re-Use Analysis for GPUs (J. Dommes)
• Custom Hardware Accelerator for the Smith-Waterman Algorithm (L. M. Selinka)