Curriculum Vitae

Education

• 2022 - present: PhD Student at Friedrich-Alexander-Universität Erlangen-Nürnberg, Department Hardware-Software-Co-Design
• 2019 - 2021: M.S. in Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg
  • Thesis: Energy Efficient Deployment of Deep Learning Applications on Restricted Hardware using Deep Compression (in cooperation with Fraunhofer IIS Nuremberg)
  • Relevant Coursework: Artificial Intelligence, Deep Learning, Operating Systems Design, Distributed Systems, Virtual Machines
• 2015 - 2019: B.S. in Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg
  • Thesis: Android Inter Process Communication Fuzzing
  • Relevant Coursework: Computer Graphics, Embedded Systems, Hyperperformance Computing

Work Experience

• 2022 - present: Researcher at Friedrich-Alexander-Universität Erlangen-Nürnberg, Department Hardware-Software-Co-Design and Fraunhofer IIS, Group Efficient AI
  • Automated design, compression, and deployment of deep neural networks (DNNs) on Cortex-M microcontrollers for image and time series classification and regression tasks
  • On-device training of fully quantized DNNs on Cortex-M microcontrollers
  • Design, implementation, and maintenance of three demonstrators for live audio classification, object detection, and on-device training of DNNs on Cortex-M microcontrollers
• 2020 - 2022: Research Assistant at Fraunhofer IIS, Group “Self-Learning Systems” and “Machine Learning & Validation”, Nuremberg
  • Compression of deep neural networks for deployment on restricted hardware
  • Duties includes: Updates and improvements to template
  • Evaluation of different parameter reduction, quantization, and graph optimization techniques for deep neural networks
• 2015 - 2020: Working Student at Siemens AG, Erlangen
  • Design, development and integration of a webservice into the open, cloud based IoT system Mindsphere
  • Continuous delivery and maintenance of the webservice as a tool used by Mindsphere´s system test and quality assurance departments
  • Implementation of an Android application and a web UI for the webservice
  • Implementation of automated UI tests based on the Selenium framework
• Summer 2015: Internship at Siemens AG, Erlangen
  • Design and Development of a webservice for easy test analytics based on Google’s ACC-model

Publications

Efficient Network Inference via Hardware-Aware Architecture Search, Model Pruning & Quantization

Heublein, L., Deutel, M., Plinge, A., & Ott, F. (2026). Efficient Network Inference via Hardware-Aware Architecture Search, Model Pruning & Quantization. arXiv:2606.23210.

PrototypeNAS: Rapid Design of Deep Neural Networks for Microcontroller Units

Deutel, M., Geis S., Plinge, A. (2026). PrototypeNAS: Rapid Design of Deep Neural Networks for Microcontroller Units. arXiv:2603.15106.

Recent Trends in Edge AI: Efficient Design, Training and Deployment of Machine Learning Models

Deutel, M., Mallah M., Wissing J., Scheele, S. (2026). Recent Trends in Edge AI: Efficient Design, Training and Deployment of Machine Learning Models. Charting the Intelligence Frontiers – Edge AI Systems Nexus.

Unsupervised Learning of Variational Autoencoders on Cortex-M Microcontrollers

Deutel, M., Plinge, A., Seuß, D., Mutschler, C., Hannig, F., & Teich, J. (2025). Unsupervised Learning of Variational Autoencoders on Cortex-M Microcontrollers. IEEE 18th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC).

Early-Exit Neural Architecture Search for Energy-Harvesting Edge Computing

Sixdenier, P., Deutel, M., & Teich, J. (2025). Early-Exit Neural Architecture Search for Energy-Harvesting Edge Computing. IEEE 18th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC).

Multi-Objective Bayesian Optimization with Reinforcement Learning for Edge Deployment of DNNs on Microcontrollers

Deutel, M., Kontes, G., Mutschler, C., & Teich, J. (2025). Multi-Objective Bayesian Optimization with Reinforcement Learning for Edge Deployment of DNNs on Microcontrollers. Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO).

Combining Multi-Objective Bayesian Optimization with Reinforcement Learning for TinyML

Deutel, M., Kontes, G., Mutschler, C., & Teich, J. (2025). Combining Multi-Objective Bayesian Optimization with Reinforcement Learning for TinyML. ACM Transactions on Evolutionary Learning and Optimization.

Fused-Layer CNNs for Memory-Efficient Inference on Microcontrollers

Deutel, M., Hannig F., Mutschler, C., & Teich, J. (2024). Fused-Layer CNNs for Memory-Efficient Inference on Microcontrollers. In Workshop on Machine Learning and Compression @ NeurIPS.

Energy-Efficient AI on the Edge

Witt, N., Deutel, M., Schubert, J., Sobel, C., Woller, P. (2024). Energy-Efficient AI on the Edge. In: Mutschler, C., Münzenmayer, C., Uhlmann, N., Martin, A. (eds) Unlocking Artificial Intelligence. Springer, Cham.

On-Device Training of Fully Quantized Deep Neural Networks on Cortex-M Microcontrollers

Deutel, M., Hannig, F., Mutschler, C., & Teich, J. (2024). 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.

microYOLO: Towards Single-Shot Object Detection on Microcontrollers

Deutel, M., Mutschler, C., & Teich, J. (2023). microYOLO: Towards Single-Shot Object Detection on Microcontrollers. In ECML PKDD Conference 2023, at the 4th Workshop on IoT, Edge, and Mobile for Embedded Machine Learning.

Energy-efficient Deployment of Deep Learning Applications on Cortex-M based Microcontrollers using Deep Compression

Deutel, M., Woller, P., Mutschler, C., & Teich, J. (2023, March). Energy-efficient Deployment of Deep Learning Applications on Cortex-M based Microcontrollers using Deep Compression. In MBMV 2023; 26th Workshop (pp. 1-12). VDE.

Service

• I have served as a reviewer for NeurIPS’24 ‘25 and ‘26, ICLR’25, TMLR, ACM Transactions on Embedded Computing Systems, IEEE Internet of Things Journal, and Springer Journal of Real-Time Image Processing.