An integrated approach of Genetic Algorithm and Machine Learning for generation of Worst-Case Data for Real-Time Systems

Kumar, V (2022) An integrated approach of Genetic Algorithm and Machine Learning for generation of Worst-Case Data for Real-Time Systems. In: 26th IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications, DS-RT 2022, 26 - 28 September 2022, Ales, pp. 87-95.

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Abstract

Determining Worst-Case Execution Time (WCET) is essential for temporal verification of Real-Time and Embedded Systems. These systems are designed to meet the stringent timing constraints imposed by the regulations. If a system gets delayed due to non-compliance with the deadline, it will lead to disastrous events. Worst-Case Data which gives maximum execution time, plays a vital role in the estimation of WCET. An evolutionary algorithm such as the Genetic Algorithm has been employed to generate the Worst-Case Data. The complexity of an evolutionary algorithm requires the use of several computational resources. This paper presents a novel method to replace the hardware and simulator used in the evolution process with machine learning models. This method reduces the overall time required to generate Worst-Case Data. Different machine learning models are trained to integrate with genetic algorithms. Our machine learning models are created using the Pygad Framework. The feasibility of the proposed approach is validated using benchmarks from different domains. The results show the speedup in the generation of Worst-Case Data. © 2022 IEEE.

Item Type:	Conference Paper
Publication:	2022 IEEE/ACM 26th International Symposium on Distributed Simulation and Real Time Applications, DS-RT 2022
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	The copyright for this article belongs to Institute of Electrical and Electronics Engineers Inc.
Keywords:	Computer aided design; Embedded systems; Interactive computer systems; Learning algorithms; Machine learning; Real time systems, Algorithm learning; Embedded-system; Integrated approach; Machine learning models; Machine-learning; Real - Time system; Real-time and embedded systems; Stringents; Temporal verification; Worst-case execution time, Genetic algorithms
Department/Centre:	Division of Interdisciplinary Sciences > Computational and Data Sciences
Date Deposited:	04 Jan 2023 05:19
Last Modified:	04 Jan 2023 05:19
URI:	https://eprints.iisc.ac.in/id/eprint/78693

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