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Artificial intelligence (AI) is an important research field in digitalization and has the potential to sustain growth and prosperity in a disruptive way. The role of special hardware for AI is still underdeveloped: while companies such as Xilinx, NVIDIA, ARM and Intel are integrating more and more AI elements into their platforms, AI hardware aspects are hardly covered in university teaching. Dedicated AI-capable hardware is particularly relevant in Germany because, in contrast to the alternative of cloud processing, it does not rely on collecting data in the cloud, which is problematic from a data protection perspective. In order to combine our socio-political values such as the right to privacy, informational self-determination and data federalism with data-driven innovations (Industry 4.0, 6G, Smart Cities, autonomous driving, IoT, monitoring using remote sensing and geospatial data), the “Edge Computing.” Here, some highly sensitive information is processed at the point of origin (“network edge”) and only necessary information is transmitted. Current university teaching only touches this AI hardware topic minimally due to separate sub-disciplines. This is where we want to start our project. We would like to address a multidisciplinary target group (from hardware, AI and applications) in an integrative (through open courses, practice-oriented teaching and teamwork) and realistic (chip production in Germany, also as part of courses by students) with an attractive range of courses. Specifically, we are planning a hybrid, cross-university educational offering that combines theoretical principles, design and exemplary application. Our consortium (with the associated Leibniz Institute IHP in Frankfurt/Oder) has the unique chip manufacturing capability in Germany, which allows students to put AI hardware into practice during their studies.

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