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Runtime WCET Estimation Using Machine Learning

Authors
Yun,SangwoonKang, Kyungtae
Issue Date
Oct-2023
Publisher
ACM
Keywords
embedded systems; real-time systems; neural networks
Citation
ACM MobiCom '23: Proceedings of the 29th Annual International Conference on Mobile Computing and Networking, no.133, pp 1 - 3
Pages
3
Indexed
FOREIGN
Journal Title
ACM MobiCom '23: Proceedings of the 29th Annual International Conference on Mobile Computing and Networking
Number
133
Start Page
1
End Page
3
URI
https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/117685
DOI
10.1145/3570361.3615740
Abstract
Accurate task execution time estimation is vital for efficient and dependable operation of safety-critical systems. However, modern automotive functions' complexity challenges conventional estimation methods. To address this, we propose a novel technique that combines execution time and job sequence data using a multi-layer perceptron (MLP) neural network. Leveraging MLP's capabilities, our approach achieves impressive 99.7% prediction accuracy with a mere 38.33 μs latency. Integrating our technique into safety-critical systems optimizes resource allocation and scheduling, enhancing performance and reliability. Importantly, our method extends beyond automotive systems, finding potential in diverse safety-critical domains. By precisely estimating task execution time, we enhance operational efficiency and decision-making in complex systems.
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