TY - GEN
T1 - Measurement Methods for Software Execution Time on Heterogeneous Edge Devices
AU - Rupprecht, Bernhard
AU - Vogel-Heuser, Birgit
AU - Neumann, Eva Maria
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Edge Benchmarking
KW - Execution Time Measurement
KW - Programmable Logic Controllers (PLCs)
UR - http://www.scopus.com/inward/record.url?scp=85171142278&partnerID=8YFLogxK
U2 - 10.1109/INDIN51400.2023.10218136
DO - 10.1109/INDIN51400.2023.10218136
M3 - Conference contribution
AN - SCOPUS:85171142278
T3 - IEEE International Conference on Industrial Informatics (INDIN)
BT - 2023 IEEE 21st International Conference on Industrial Informatics, INDIN 2023
A2 - Dorksen, Helene
A2 - Scanzio, Stefano
A2 - Jasperneite, Jurgen
A2 - Wisniewski, Lukasz
A2 - Man, Kim Fung
A2 - Sauter, Thilo
A2 - Seno, Lucia
A2 - Trsek, Henning
A2 - Vyatkin, Valeriy
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 21st IEEE International Conference on Industrial Informatics, INDIN 2023
Y2 - 17 July 2023 through 20 July 2023
ER -