Towards a Quantitative Time Analysis and Decision Support for the Deployment of AI-Algorithms in Distributed Cyber-Physical Production Systems

Modern Cyber-Physical Production Systems get more and more intelligent by higher capacities of the used resources and more resource-efficient AI-algorithms. However, a significant challenge is finding the fitting architecture for hardware and software cost-efficiently and with low effort. Currently,...

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Bibliographic Details
Published inIECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society pp. 1 - 6
Main Authors Hujo, Dominik, Vogel-Heuser, Birgit, Kruger, Marius, Schuhmann, Fabian
Format Conference Proceeding
LanguageEnglish
Published IEEE 13.10.2021
Subjects
Computer and Control Sysgtems
Control systems
Embedded control
Fitting
Hardware
Industrial electronics
Production systems
Real-time information systems
Software
Software algorithms
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Summary:Modern Cyber-Physical Production Systems get more and more intelligent by higher capacities of the used resources and more resource-efficient AI-algorithms. However, a significant challenge is finding the fitting architecture for hardware and software cost-efficiently and with low effort. Currently, this process consists of trial and error or selecting overpowered hardware resources, which leads to expensive and time-consuming processes in the development. This paper deals with a quantitative benchmark of the timing behavior of selected algorithms for preprocessing to enable AI on representative hardware platforms in cyber-physical production systems, building on previous approaches that take a model-based view of hardware/software co-design. This approach is a first step away from a purely qualitative system design, towards a quantitative approach.
ISSN:2577-1647
DOI:10.1109/IECON48115.2021.9589515