I’m a graduate research assistant working on latency prediction models for AI workloads executed on accelerator hardware, enabling fast optimization loops for neural network architecture search (NAS) and pre-silicon hardware evaluation.
My expertise includes:
- ⚡ 8+ years of Verilog programming and FPGA synthesis
- 🤖 8+ years in Deep Neural Network applications and deployment on resource constrained devices
- 👨💻 8+ years of C/C++ programming for high-performance computing and embedded devices
- 🐍 10+ years of Python programming for scientific analysis
- 🐧 19+ years of Linux usage and programming (I ordered my first Ubuntu 5.04 install CD directly from Canonical in 2005)
My colleagues describe me as a detail-oriented analytical thinker with a passion for learning new things. I am a team player because I enjoy tackling challenges as a group and discussing problem solutions from different angles to learn from others and share my knowledge.
I am currently working on my PhD thesis at the embedded systems group (Prof. Bringmann) at the University of Tübingen.
Publications (Selection)
M. M. Müller, K. Lübeck, A. L.-F. Jung, and O. Bringmann, “FlexiSAGA: A Flexible Systolic Array GEMM Accelerator for Sparse and Dense Processing”, in Proceedings of SAMOS 2025, Vathy. PDF
K. Lübeck, A. L.-F. Jung, F. Wedlich, M. M. Müller, F. N. Peccia, F. Thömmes, J. Steinmetz, V. Biermaier, A. Frischknecht, P. P. Bernardo, and O. Bringmann, “Automatic Generation of Fast and Accurate Performance Models for Deep Neural Network Accelerators”, in ACM Transactions of Embedded Computing Systems (TECS), 2025. PDF
M. M. Müller, A. R. M. Borst, K. Lübeck, A. L.-F. Jung, and O. Bringmann, “Using the Abstract Computer Architecture Description Language to Model AI Hardware Accelerators”, in MBMV 2024, Kaiserslautern. PDF
C. Gerum, A. Frischknecht, T. Hald, P. P. Bernardo, K. Lübeck, and O. Bringmann, “Hardware Accelerator and Neural Network Co-Optimization for Ultra-Low-Power Audio Processing Devices,” in 25th Euromicro Conference on Digital System Design (DSD), pp. 365–369, Maspalomas, 2022. PDF
K. Lübeck, A. L.-F. Jung, F. Wedlich, and O. Bringmann, “Work-in-Progress: Ultra-fast yet Accurate Performance Prediction for Deep Neural Network Accelerators,” in ACM/IEEE International Conference on Compilers, Architectures, and Synthesis for Embedded Systems (CASES), Shanghai, 2022. PDF
P. P. Bernardo, C. Gerum, A. Frischknecht, K. Lübeck, and O. Bringmann, “UltraTrail: A Configurable Ultra-Low Power TC-ResNet AI Accelerator for Efficient Keyword Spotting,” in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), pages 1-12, 2020. PDF
K. Lübeck and O. Bringmann, “A Heterogeneous and Reconfigurable Embedded Architecture for Energy-Efficient Execution of Convolutional Neural Networks,” in ARCS 2019: Architecture of Computing Systems, Copenhagen, 2019.