Abstract
Machine learning algorithms play a significant role for the realization of industrial analytics functions, such as predictive maintenance. This paper first outlines the workflow and topology variants for industrial analytics, and then focuses on the efficient realization of machine learning algorithms on edge devices using reconfigurable System-on-Chip architectures, showing the benefits of an optimized application-specific realization.
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Gatica, C.P., Platzner, M. (2020). Adaptable Realization of Industrial Analytics Functions on Edge-Devices using Reconfigurable Architectures. In: Beyerer, J., Maier, A., Niggemann, O. (eds) Machine Learning for Cyber Physical Systems. Technologien für die intelligente Automation, vol 11. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-59084-3_9
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DOI: https://doi.org/10.1007/978-3-662-59084-3_9
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