Runtime WCET Estimation Using Machine Learning
- Title
- Runtime WCET Estimation Using Machine Learning
- Author
- 강경태
- Keywords
- embedded systems; real-time systems; neural networks
- Issue Date
- 2023-10
- Publisher
- ACM
- Citation
- ACM MobiCom '23: Proceedings of the 29th Annual International Conference on Mobile Computing and Networking, article no 133, page. 1525-1527
- 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 decisionmaking in complex systems.
- URI
- https://dl.acm.org/doi/10.1145/3570361.3615740https://repository.hanyang.ac.kr/handle/20.500.11754/190718
- DOI
- https://doi.org/10.1145/3570361.3615740
- Appears in Collections:
- ETC[S] > 연구정보
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