Due to emerging data-driven approaches in factory automation in the course of Industry 4.0, automated Production Systems must incorporate additional algorithms for data collection and processing tasks. However, strict real-time requirements, resource constraint devices, such as Programmable Logic Controllers or low-power edge devices, and network bandwidth limitations pose a challenge to selecting suitable algorithms for specific edge devices and vice-versa, also known as software-hardware co-design. Measuring the execution time of an algorithm or code snippet is therefore a crucial part of algorithm and hardware assessment and is incorporated in numerous benchmarks. However, this is not trivial since most existing time measurement methods are designed with specific devices in mind with limited portability to different hardware platforms. Thus this paper provides an overview of the properties of selected execution time measurement methods to support their feasible deployment in edge computing, including legacy systems.
A time measurement code snippet for Beckhoff Programmable Logic Controllers and recommendations for implementing software-based timing functions for heterogeneous devices help shorten development times. Besides, comparing execution time measurement methods highlights the challenges of creating a generic cross-platform benchmark in future research.
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Due to emerging data-driven approaches in factory automation in the course of Industry 4.0, automated Production Systems must incorporate additional algorithms for data collection and processing tasks. However, strict real-time requirements, resource constraint devices, such as Programmable Logic Controllers or low-power edge devices, and network bandwidth limitations pose a challenge to selecting suitable algorithms for specific edge devices and vice-versa, also known as software-hardware co-de...
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