Efficient AI

Sei es für die Automatisierung von Prozessen, sei es für die Analyse großer Datenmengen: Intelligente und selbstlernende Systeme werden in Unternehmensprozessen zunehmend wichtiger. Bisher müssen diese intelligenten Systeme stets in Verbindung mit einer Cloud stehen, da diese die benötigte Rechenleistung für KI-Modelle bereitstellt. Mit Edge AI, kurz für Edge Artificial Intelligence, kommt nun die nächste Generation von intelligenten Systemen ins Haus: Die Intelligenz wird dabei direkt in die Endgeräte verlagert.

Schubert, J., Kasper, M., Linke, M., Herzog, B., Deutel, M., Plinge, A., Seuss, D., Mutschler, C. (2026):

Design-Time Optimization of Deep Neural Networks for Intermittent Learning on Microcontrollers

in: arXiv: 2608.03589

Wittenberg, T., Kraus, M., Fiedelak, S., Keck, S., Kiderle, T., Hallmen, T., plinge, A., Niessen, C., Lang-Richter, N., André, E., Manzeschke, A. (2026):

Where can speaking robots be found in healthcare- and what are challanges for their implementation? 

Heublein, L., Deutel, M., Plinge, A., Ott, F. (2026): 

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

in: arXiv: 2606.23210

Deutel, M., Marchl, A., Plinge, A., Hannimg, F., Teich, J. (2026):

EmbeddedAD: Efficient Anomaly Detection on SoCs via Vector-Quantized Reverse Distillation

In: IEEE International System-on-Chip Conference (SOCC) 

Deutel, M., Geis, S., Plinge A. (2026):

PrototypeNAS: Rapid Design of Deep Neural Networks for Microcontroller Units

In:  arXiv:2603.15106

Jain, P., Kasper, M., Köber, G., Amft, O., Plinge, A., & Seuss, D. (2025):

Pareto optimal benchmarking of AI models on ARM Cortex processors for sustainable embedded systems.

In: European Conference on EDGE AI Technologies and Applications (EEAI 2025)

Brolich, N., Geis, S., Kasper, M., Barnhill, A., Plinge, A., & Seuss, D. (2025).:

Investigating target class influence on neural network compressibility for energy-autonomous avian monitoring. 

In: European Conference on EDGE AI Technologies and Applications (EEAI 2025).

Marchl, A., Kasper, M. Geis, S., Deutel, M., Plinge A., & Seuß, D. (2025):

Anomaly Detection on the Edge for Quality Inspection. 

In: AI in Production Workshop at KI2025

Deutel, M., Plinge, A., Seuß, D., Mutschler, C., Hannig, F., & Teich, J. (2025):

Unsupervised learning of variational autoencoders on Cortex-M microcontrollers.

In: IEEE International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC 2025), Singapore

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)