Mark Deutel

Chair of Computer Science 12 (Hardware/Software Co-Design)

Research associates

Address

Cauerstraße 1191058 Erlangen

Contact

Email: mark.deutel@fau.de

Curriculum Vitæ

Since 05/2022

Researcher at the Department of Computer Science 12 (Hardware/Software Co-Design)

2019 – 2022

Computer Science M.Sc. at Friedrich-Alexander-Universität Erlangen-Nürnberg

2015 – 2019

Computer Science B.Sc. at Friedrich-Alexander-Universität Erlangen-Nürnberg

Research Projects

Currently None

Research Interests

Multi-Objective Optimization of Deep Neural Networks (DNNs) using Bayesian Optimization
- Design space exploration of DNN hyper- and compression parameters for optimized deployment on microcontrollers
- Neural Architecture Search (NAS), scalable and efficient architectures
On-Device Continual Learning for Microcontrollers
- (Class-)Incremental Learning
- Lifelong Learning
TinyML – Machine Learning on the Edge
- Efficient DNN Inference on (Cortex-M) microcontroller targets
- DNN Pruning & Quantization
- Demos and use-cases demonstrating the potential of embedded AI

Demos & Other Resources

Demo of MicroYOLO person detection on a STM32H743VI ARM Cortex-M7 based microcontroller (OpenMV H7 R2)

Demo of On-Device DNN Training (Animal Classification) on a STM32H743VI ARM Cortex-M7 based microcontroller (OpenMV H7 R2)

Teaching

SEMESTER

Multi-Core Architectures and Programming

Open Theses

Currently none

Supervised Theses

Currently none

Publications

2025

Deutel M., Kontes G., Mutschler C., Teich J.:
Combining Multi-Objective Bayesian Optimization with Reinforcement Learning for TinyML
In: ACM Transactions on Evolutionary Learning and Optimization 5 (2025), p. 1-21
ISSN: 2688-299X
DOI: 10.1145/3715012
BibTeX: Download
Deutel M., Kontes G., Mutschler C., Teich J.:
Multi-Objective Bayesian Optimization with Reinforcement Learning for Edge Deployment of DNNs on Microcontrollers
The Genetic and Evolutionary Computation Conference (GECCO) (Málaga, 14. July 2025 – 18. July 2025)
In: ACM (ed.): GECCO ’25 Companion: Proceedings of the Genetic and Evolutionary Computation Conference Companion 2025
DOI: 10.1145/3712255.3734232
BibTeX: Download
Deutel M., Mutschler C., Teich J.:
microYOLO: Towards Single-Shot Object Detection on Microcontrollers
4th Workshop on IoT, Edge, and Mobile for Embedded Machine Learning (ITEM) (Torino, 18. September 2023 – 22. September 2023)
In: University of Turin (ed.): International Workshops of ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Revised Selected Papers, Part V 2025
DOI: 10.1007/978-3-031-74643-7_13
BibTeX: Download
Deutel M., Plinge A., Seuss D., Mutschler C., Hannig F., Teich J.:
Unsupervised Learning of Variational Autoencoders on Cortex-M Microcontrollers
IEEE 18th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC) (Singapore, 15. December 2025 – 18. December 2025)
In: IEEE 18th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC) 2025
DOI: 10.1109/MCSoC67473.2025.00079
BibTeX: Download
Sixdenier PL., Deutel M., Teich J.:
Early-Exit Neural Architecture Search for Energy-Harvesting Edge Computing
2025 IEEE 18th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC) (Singapore, 15. December 2025 – 18. December 2025)
In: 2025 IEEE 18th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC) 2025
DOI: 10.1109/MCSoC67473.2025.00066
URL: https://ieeexplore.ieee.org/document/11311064
BibTeX: Download

2024

Deutel M., Hannig F., Mutschler C., Teich J.:
Fused-Layer CNNs for Memory-Efficient Inference on Microcontrollers
Workshop on Machine Learning and Compression @ NeurIPS 2024 (Vancouver Convention Center, 10. December 2024 – 15. December 2024)
Open Access: https://openreview.net/forum?id=2O8qbyxH6X
BibTeX: Download
Deutel M., Hannig F., Mutschler C., Teich J.:
On-Device Training of Fully Quantized Deep Neural Networks on Cortex-M Microcontrollers
In: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 44 (2024), p. 1250 – 1261
ISSN: 0278-0070
DOI: 10.1109/TCAD.2024.3484354
URL: https://ieeexplore.ieee.org/document/10726519
BibTeX: Download
Witt N., Deutel M., Sobel C., Schubert J., Woller P.:
Energy-Efficient AI on the Edge
In: Christopher Mutschler, Christian Münzenmayer, Norman Uhlmann, Alexander Martin (ed.): Unlocking Artificial Intelligence: From Theory to Applications, Springer Link, 2024, p. 359 – 380
DOI: 10.1007/978-3-031-64832-8_19
BibTeX: Download

2023

Deutel M., Woller P., Mutschler C., Teich J.:
Energy-efficient Deployment of Deep Learning Applications on Cortex-M based Microcontrollers using Deep Compression
Methoden und Beschreibungssprachen zur Modellierung und Verifikation von Schaltungen und Systemen MBMV’23 (Freiburg, 23. March 2023 – 24. March 2023)
In: VDE (ed.): MBMV 2023; 26th Workshop 2023
Open Access: https://arxiv.org/abs/2205.10369
URL: https://ieeexplore.ieee.org/document/10173060
BibTeX: Download