CV - Mark Deutel

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: Research Assistant 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: Student 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

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. The Genetic and Evolutionary Computation Conference (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.

Mark Deutel

CV