The increasing demands of complexity, flexibility and self-organization of networked embedded systems require new solutions for the local processor architectures and firmware. This includes the ability to adapt to the requirements of an algorithm e.g. in relation to a sensor value measured by an Cyberphysical System. Artificial intelligence becomes ubiquitous and therefore enters also the domain of embedded systems and enables enormous opportunities in edge computing. This will introduce new degrees of freedom in embedded and Cyberpyhsical systems and is very promising to support the aforementioned demands tremendously. In this talk, possible solutions for next generation embedded systems with AI components will be introduced.
Michael Huebner is a Full Professor and leads the Chair for Computer Engineering at the Brandenburg University of Technology (BTU) in Cottbus, Germany, since 2018.
He is also Vice-President for research and transfer at BTU since December 2020.
He received his diploma degree in electrical engineering and information technology in 2003, his Ph.D. degree in 2007 from the University of Karlsruhe (TH), and did his habilitation in 2011 at the Karlsruhe Institute of Technology (KIT) in the domain of reconfigurable computing systems.
His research interests are in reconfigurable computing and particularly new technologies for adaptive FPGA run-time reconfiguration and on-chip network structures with application in automotive systems, incl. the integration into high-level design and programming environments.
Virtual: https://events.vtools.ieee.org/m/285156