Core topic Embedded AI
AI algorithms and methods unsuitable for embedded systems
Usually, embedded systems are specifically adapted to their task such as data acquisition or control or signal processing in a device and optimised for minimal cost, space, energy and memory consumption. The numerous existing AI algorithms and methods, in contrast, usually operate on much more powerful, larger, more energy-intensive and more expensive computing technology and are unsuitable for embedded systems. In addition, there are often too few data sets available for applications to train an AI, such as from fault situations on a machine for which predictive maintenance is to be supported with AI.
We optimise AI algorithms and methods for embedded systems
We are researching to optimise AI algorithms and methods so that they can be used on embedded systems. For predictive maintenance applications, for example, we are working on generating artificial data, increasing robustness and transferring the solutions to similar problems.
Dr.-Ing. Tino Hutschenreuther
Head of System Design
tino.hutschenreuther(at)imms.de+49 (0) 3677 874 93 40
Dr. Tino Hutschenreuther will answer your questions on our research in Smart distributed measurement and test systems and the related core topics Analysis of distributed IoT systems, Embedded AI and Real-time data processing and communications, on the lead applications Adaptive edge AI systems for industrial application and IoT systems for cooperative environmental monitoring as well as on the range of services for the development of embedded systems.
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Industry 4.0 communications for an assistance system, ML-based, that autonomously analyses connected production chains
Dr. Alexander Maier, Fraunhofer IOSB-INA
“We very much appreciate not only the sound, practical engineering knowledge of the IMMS staff concerning Industry 4.0-compatible protocols and systems but also the personal contact and the constructive manner of IMMS’ collaboration. And so we shall be delighted if we can tackle upcoming subjects together.”
Towards Deploying DNN Models on Edge for Predictive Maintenance ApplicationsRick Pandey1. Sebastian Uziel1. Tino Hutschenreuther1. Silvia Krug1,2.
Electronics 2023, 12(3), 639; doi.org/10.3390/electronics120306391IMMS Institut für Mikroelektronik- und Mechatronik-Systeme gemeinnützige GmbH, 98693 Ilmenau, Germany. 2Department of Computer and Electrical Engineering, Mid Sweden University, Holmgatan 10, 851 70 Sundsvall, Sweden.
KI für energieeffiziente Sensorsysteme – Effiziente Überwachung von Maschinen und AnlagenSebastian Uziel1.
elmug4future, Technologiekonferenz, 27. - 28. September 2022, Friedrichroda, Thüringen1IMMS Institut für Mikroelektronik- und Mechatronik-Systeme gemeinnützige GmbH, Ehrenbergstraße 27, 98693 Ilmenau, Germany.
Trade-off between Spectral Feature Extractors for Machine Health Prognostics on MicrocontrollersUmut Onus1. Sebastian Uziel1. Tino Hutschenreuther1. Silvia Krug1,2.
2022 IEEE 9th International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), 2022, pp. 1-6, DOI: doi.org/10.1109/CIVEMSA53371.2022.9853642, 15 - 17 June 2022, Chemnitz, Germany1IMMS Institut für Mikroelektronik- und Mechatronik-Systeme gemeinnützige GmbH, 98693 Ilmenau, Germany. 2Mid Sweden University, Sundsvall, Sweden.
Lösungsvorstellung zur Detektion von Druckluftleckage und Optimierung von DruckluftanlagenPeter Otto1. Peter Holstein2. Sebastian Uziel3.
3. Industrieforum „Smarte Fertigung. Integration flexibler Fertigungstechnologien und intelligenter Prozessketten“, 21.01.2021, Online1Postberg + Co. GmbH, 34121 Kassel. 2SONOTEC GmbH, 06112 Halle, Germany. 3IMMS Institut für Mikroelektronik- und Mechatronik-Systeme gemeinnützige GmbH, 98693 Ilmenau, Germany.