Runtime WCET Estimation Using Machine Learning
- Authors
- Yun,Sangwoon; Kang, 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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