Konstantin Lübeck
Konstantin Lübeck
University of Tübingen
Dpt. of Computer Science
Embedded Systems
Sand 13
72076 Tübingen
Germany
- Telephone
- +49 - (0) 70 71 - 29 - 78998
- Telefax
- +49 - (0) 70 71 - 29 - 50 62
- Office
- Sand 13, B225
- Office hours
- Open Door Policy
Konstantin Lübeck is a graduate research assistant at the Chair for Embedded Systems of the University of Tübingen. He received B.Sc. and M.Sc. degrees in Computer Science from the University of Tübingen in 2015 and 2018 respectively with a major focus on computer engineering and embedded systems. In 2016, he studied at the Uppsala University as an Erasmus exchange student. In 2017, he received a scholarship for master’s thesis from the Stiftung Industrieforschung. Since 2018, he is a lecturer of computer architecture for the Bosch Learning Company initiative at the technology transfer center Tübingen. In addition to his academic work, he is a trained mechatronics technician (certified by the German chamber of industry and commerce, IHK) and worked as a self-employed software developer.
Research Interests
Modeling, evaluation, and prediction of machine learning accelerator performance (end-to-end latency, throughput, roofline) using analytical models based on computer architecture descriptions (from register-transfer level to abstract block diagrams) and deep neural network parameters (layer types and their hyperparameters) for fast design space explorations used in neural network-hardware co-design algorithms.
Publications
2025
Automatic Generation of Fast and Accurate Performance Models for Deep Neural Network Accelerators
by Konstantin Lübeck, Alexander Louis-Ferdinand Jung, Felix Wedlich, Mika Markus Müller, Federico Nicolás Peccia, Felix Thömmes, Jannik Steinmetz, Valentin Biermaier, Adrian Frischknecht, Paul Palomero Bernardo, and Oliver BringmannIn ACM Trans. Embed. Comput. Syst. 24(2). Association for Computing Machinery, 2025.
Keywords: Deep neural networks, performance estimation, analytical model
2024
It’s all about PR – Smart Benchmarking AI Accelerators using Performance Representatives
by Alexander Louis-Ferdinand Jung, Jannik Steinmetz, Jonathan Gietz, Konstantin Lübeck, and Oliver BringmannPeer-reviewed publication, 2024.
Using the Abstract Computer Architecture Description Language to Model AI Hardware Accelerators
by Mika Markus Müller, Alexander Richard Manfred Borst, Konstantin Lübeck, Alexander Louis-Ferdinand Jung, and Oliver BringmannPeer-reviewed publication, 2024.
2022
Hardware Accelerator and Neural Network Co-Optimization for Ultra-Low-Power Audio Processing Devices
by Christoph Gerum, Adrian Frischknecht, Paul Palomero Bernardo, Tobias Hald, Konstantin Lübeck, and Oliver BringmannIn 2022 25th Euromicro Conference on Digital System Design (DSD), pages 1-8, 2022.
Keywords: Machine Learning, Neural Networks, AutoML, Neural Architecture Search
Work-in-Progress: Ultra-fast yet Accurate Performance Prediction for Deep Neural Network Accelerators
by Konstantin Lübeck, Alexander Louis-Ferdinand Jung, Felix Wedlich, and Oliver BringmannIn 2022 International Conference on Compilers, Architectures, and Synthesis for Embedded Systems (CASES), pages 27-28, 2022.
Keywords: Machine Learning, Neural Networks, Performance Prediction, Neurnal Network Accelerators
2021
APPEL - AGILA ProPErty and Dependency Description Language
by Christoph Grimm, Frank Wawrzik, Alexander Jung, Konstantin Luebeck, Sebastian Post, Johannes Koch, and Oliver BringmannIn Proceedings Methoden und Beschreibungssprachen zur Modellierung und Verifikation von Schaltungen und Systemen (MBMV) Workshop 2021, 2021.
2020
UltraTrail: A Configurable Ultra-Low Power TC-ResNet AI Accelerator for Efficient Keyword Spotting
by Paul Palomero Bernardo, Christoph Gerum, Adrian Frischknecht, Konstantin Lübeck, and Oliver BringmannIn 2020 IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), pages 1-12, 2020.
2019
A Heterogeneous and Reconfigurable Embedded Architecture for Energy-Efficient Execution of Convolutional Neural Networks
by Konstantin Lübeck and Oliver BringmannIn Architecture of Computing Systems (ARCS 2019), 2019.
2016
Neues Konzept zur Steigerung der Zuverlaessigkeit einer ARM-basierten Prozessorarchitektur unter Verwendung eines CGRAs
by Konstantin Luebeck, David Morgenstern, Thomas Schweizer, Dustin Peterson, Wolfgang Rosenstiel, and Oliver BringmannIn Proceedings Methoden und Beschreibungssprachen zur Modellierung und Verifikation von Schaltungen und Systemen (MBMV) Workshop 2016, 2016.
Research projects
Teaching
| Advanced Topics in Embedded Systems | Summer 2019 |
|---|---|
| Efficient Machine Learning in Hardware | Summer 2021 Summer 2022 |
| Entwurf und Synthese Eingebetteter Systeme | Summer 2019 |
| Moderne Architekturen Eingebetteter Systeme | Winter 2018 Winter 2019 Winter 2020 |
| Parallele Rechnerarchitekturen | Summer 2020 Summer 2021 Winter 2022 Winter 2023 |
| Proseminar: Moderne Architekturen Eingebetteter Systeme | Winter 2021 |
| Teamprojekt: Implementierung einer RISC-V Rechnerarchitektur | Winter 2024 |
| Teamprojekt: Interaktiver Rechnerarchitektursimulator | Summer 2023 |
| Teamprojekt: Weiterentwicklung einer RISC-V Rechnerarchitektur | Summer 2025 |