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.
Contact
Contact
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.
Related content
Project
VirtuSen
With the help of virtual sensor technology and AI, it is intended to detect and compensate for disturbing influences on complex mechatronic systems such as high-precision drives with spatial resolution
Project
ProQuaOpt
IMMS is developing an AI-based control system for resource-efficient online optimisation of injection moulding
Project
thurAI
In thurAI, IMMS is working on sensor technology for SmartCity and methods to intelligently process data in the network for AI evaluations.
Project
KIQ
IMMS has developed an AI-based, retrofittable and cost-effective solution for quality assurance of machining tools.
Reference
Frank Seiferth, seioTec GmbH
”In our solution-oriented collaboration with IMMS, we were able to benefit in particular from the expertise of IMMS employees in sensor and embedded systems technology.“
Reference
Peter Otto, Postberg
“We are very satisfied with the result – the many years of IMMS experience in the design of embedded systems, with industrial projects and also the system understanding of IMMS from the previous product developments with SONOTEC have clearly contributed to the success.”
Reference
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.”
Development and validation of a simplified coil model for CFD simulation of a nano-positioning planar drive system
Ina Naujokat1. Ludwig Herzog1. Steffen Hesse1. Parastoo Salimitari1.Euspen Special Interest Conference: Precision & Performance 2025, November 18-20, 2025, Cranfield University, United Kingdom
1IMMS Institut für Mikroelektronik- und Mechatronik-Systeme gemeinnützige GmbH, Ehrenbergstraße 27, 98693 Ilmenau, Germany.Experimental characterization of thermal disturbances in a 6DOF nanopositioning system under varying operational conditions
Parastoo Salimitari1. Steffen Hesse1. Michael Katzschmann1. Luwig Herzog1.Euspen Special Interest Conference: Precision & Performance 2025, November 18-20, 2025, Cranfield University, United Kingdom
1IMMS Institut für Mikroelektronik- und Mechatronik-Systeme gemeinnützige GmbH, Ehrenbergstraße 27, 98693 Ilmenau, Germany.Smartes Maschinenmonitoring – Anomalien mit Edge-KI detektieren
Sebastian Uziel1.in elektroniknet.de, 11. November, S. 37 - 40, www.elektroniknet.de/automation/industrie-40-iot/anomalien-mit-edge-ki-detektieren.228572.html und in Markt&Technik, Trend Guide 2025, ePaper: wfm-publish.blaetterkatalog.de/frontend/mvc/catalog/by-name/MUT
1IMMS Institut für Mikroelektronik- und Mechatronik-Systeme gemeinnützige GmbH, Ehrenbergstraße 27, 98693 Ilmenau, Germany.Neue Ansätze in der Kunststoffproduktion – Ein KI-basiertes Assistenzsystem für den Spritzgießprozess
Dominik Schraml1. Silvia Krug2.Thüringer Maschinenbautag, Erfurt, 22. Oktober 2024
1Steinbeis Qualitätssicherung und Bilderverarbeitung GmbH, Ilmenau. 2IMMS Institut für Mikroelektronik- und Mechatronik-Systeme gemeinnützige GmbH, Ehrenbergstraße 27, 98693 Ilmenau, Germany.
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Lead application
Adaptive edge AI systems for industrial application
We are researching solutions for adaptive edge AI systems to make AI possible on low-consumption embedded systems in industry and to network them in real time.
Service for R&D
Development of embedded systems
We develop embedded systems for you as complete solutions consisting of sensors and actuators, signal processing and communications technology as well as open-loop and closed-loop controls.
Lead application
IoT systems for cooperative environmental monitoring
We are researching energy-efficient solutions for IoT systems to open up new applications for cooperative environmental monitoring, such as in agriculture.
Research field
Smart distributed measurement and test systems
Integrated sensor ICs are the heart of sensor and measurement systems like wireless sensors, stationary or handheld devices. We are researching solutions for ever more powerful sensors with more intrinsic intelligence and task allocation in the network.