Review Embedded Talk “Deep Learning on Narrow Resources”

On Friday, October 10, 2025, our 19th Embedded Talk on the topic of “Deep Learning on Narrow Resources” took place at Friedrich-Alexander University Erlangen-Nuremberg. The event, which was attended by around 60 participants, provided a platform for experts and researchers from the industry to discuss current developments and challenges in the field of machine learning methods for systems with limited resources. The event began with a welcome address by Prof. Dr. Jürgen Teich, spokesperson for the FAU Research Center ESI. Prof. Dr. Jian-Jia Chen, TU Dortmund University/Lamarr Institute for Machine Learning and Artificial Intelligence, then gave a keynote lecture on “Resource-Aware Machine Learning for Cyber-Physical Systems.” In his presentation, he highlighted the challenges and opportunities of machine learning methods in cyber-physical systems. Mark Deutel, researcher at Fraunhofer IIS and FAU Erlangen-Nuremberg, then presented his research findings on “On-Device Training of Deep Neural Networks on Cortex-M Microcontrollers.” He demonstrated how deep neural networks can be trained on Cortex-M microcontrollers.

After a short coffee break, Jan Seyler from Festo SE & Co. KG gave a presentation on “Real-World Challenges of Deploying Embedded AI.” He highlighted the challenges that arise in practice when implementing artificial intelligence in embedded systems. The event concluded with a panel discussion with all speakers and a poster session, where participants had the opportunity to exchange ideas with the speakers and other attendees. The event was a great success and provided a valuable platform for the exchange of ideas and experiences in the field of machine learning methods for cyber-physical and embedded systems.

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