Efficient AI

Whether for automating processes or analyzing large volumes of data: Intelligent and self-learning systems are becoming increasingly important in business processes. Until now, these intelligent systems have always had to be connected to a cloud, as this provides the necessary computing power for AI models. With Edge AI, short for Edge Artificial Intelligence, the next generation of intelligent systems is now entering the home: intelligence is being transferred directly to the end devices.

TinyML enables machine learning on microcontrollers and opens doors to a wide range of application areas. The course is designed for specialists with data analysis experience and hardware developers who want to improve their knowledge of machine learning.

You will learn how to develop ML applications on microcontrollers, recognize challenges at an early stage and develop appropriate strategies. Use our comprehensive know-how for your success.

In the ML seminar, you will learn the basics of machine learning using supervised and unsupervised learning methods. In addition, you will learn more about proven methods for fast results and decisions as well as examples of the use of machine learning in practical implementation.

Brans, P. & Witt, N. (2025):

Fraunhofer IIS Walks the Line for Edge AI

In: EE Times Europe Magazine

Deutel, M., Kontes, G., Mutschler, C., & Teich, J. (2025):

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

In: Genetic and Evolutionary Computation Conference (GECCO)

Mutschler, C., Münzenmayer, C., Uhlmann, N., & Martin, A (2024): 

Unlocking Artificial Intelligence: From Theory to Applications

In: Springer

Witt, N., Deutel, M., Schubert, J., Sobel, C., & Woller, P. (2024)

Energy-Efficient AI on the Edge

In: Unlocking Artificial Intelligence: From Theory to Applications (pp. 359-380)

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 2024

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 2024

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

microYOLO: Towards Single-Shot Object Detection on Microcontrollers

In: arXiv preprint arXiv:2408.15865.

Herzog, B., Schubert, J., Rheinfels, T., Nickel, C., & Hönig, T. (2024):

GreenPipe: Energy-Efficient Data-Processing Pipelines for Resource-Constrained Systems.

In: Proceedings of the 21st International Conference on Embedded Wireless Systems and Networks (EWSN'24), ACM

Yammine, G., Kontes, G., Franke, N., Plinge, A., & Mutschler, C. (2023):

Efficient Beam Search for Initial Access Using Collaborative Filtering

In: 2023 IEEE Wireless Communications and Networking Conference (WCNC) (pp. 1-6)

Adler, R., Bunte, A., Burton, S., Großmann, J., Jaschke, A., Kleen, P., ... & Wrobel, S. (2022):

Deutsche Normungsroadmap Künstliche Intelligenz

In: Fraunhofer Publica

Blauberger, P., Marzilger, R., & Lames, M. (2021):

Validation of player and ball tracking with a local positioning system

In: Sensors, 21(4), 1465

Potortì, F., Torres-Sospedra, J., Quezada-Gaibor, D., Jiménez, A. R., Seco, F., Pérez-Navarro, A., ... & Oh, H. L. (2021):

Off-line evaluation of indoor positioning systems in different scenarios: The experiences from IPIN 2020 competition

In: IEEE Sensors Journal, 22(6), 5011-5054

Löffler, C., Nickel, C., Sobel, C., Dzibela, D., Braat, J., Gruhler, B., ... & Mutschler, C. (2021):

Automated quality assurance for hand-held tools via embedded classification and AutoML

In: Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track: European Conference, ECML PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings, Part V (pp. 532-535)

Feigl, T., Kram, S., Woller, P., Siddiqui, R. H., Philippsen, M., & Mutschler, C. (2020):

RNN-aided human velocity estimation from a single IMU

In: Sensors, 20(13), 3656

Feigl, T., Gruner, L., Mutschler, C., & Roth, D. (2020):

Real-time gait reconstruction for virtual reality using a single sensor

In: 2020 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct) (pp. 84-89)

Redžepagić, A., Löffler, C., Feigl, T., & Mutschler, C. (2020):

A sense of quality for augmented reality assisted process guidance

In: 2020 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct) (pp. 129-134)

Niitsoo, A., Edelhäußer, T., Eberlein, E., Hadaschik, N., & Mutschler, C. (2019):

A deep learning approach to position estimation from channel impulse responses

In: Sensors, 19(5), 1064

Feigl, T., Kram, S., Woller, P., Siddiqui, R. H., Philippsen, M., & Mutschler, C. (2019):

A bidirectional LSTM for estimating dynamic human velocities from a single IMU

In: Computer Vision Foundation (CVF) (Eds.), Joint Workshop on Long-Term Visual Localization, Visual Odometry and Geometric and Learning-based SLAM (pp. 42-43)

Feigl, T., Mutschler, C., & Philippsen, M. (2018):

Supervised learning for yaw orientation estimation

In: 2018 international conference on indoor positioning and indoor navigation (IPIN) (pp. 206-212)

Feigl, T., Mutschler, C., & Philippsen, M. (2018):

Human compensation strategies for orientation drifts

In: 2018 IEEE Conference on Virtual Reality and 3D User Interfaces (VR) (pp. 409-414)

Feigl, T., Mutschler, C., & Philippsen, M. (2018):

Head-to-body-pose classification in no-pose VR tracking systems

In: 2018 IEEE Conference on Virtual Reality and 3D User Interfaces (VR) (pp. 1-2)