Brandenburg University of Technology Cottbus Senftenberg
New achievements in artificial machine learning are reported almost daily by the big firms. While those achievements are mainly based on high-end fast processing and massive data techniques, the potential of embedded machine learning techniques is still not understood well by the majority of the industrial players and SMEs. Nevertheless, the potential of machine learning, directly embedded in a device or system which is trained in an online or an offline fashion is perceived as very high. This has led to a broad demand by industry and SMEs for a practical and application-oriented feasibility study which helps them to understand the potential benefits but also the limitations of embedded Artificial Intelligence. Currently, the question where specific algorithms are realized, at the embedded device or in the cloud, is under discussion in several fields of applications. E.g. Xilinx supports the so called edge computing where algorithms are realized on FPGA based SoCs. On the other hand, they support also the high performance FPGA based cloud system with Amazon. Both realization alternatives have their pros and cons and need to be analyzed according the respective application domain. Furthermore, the realization in general on embedded systems is a crucial challenge for SMEs. This project aims at developing and demonstrating ‘best practices for embedded AI’ by means of four relevant industrial case studies. In those case studies we will tackle several elements which are related to technology, safety and certifiability.
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