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Publications

Journals

  1. V. Kumar, B. Ranjbar and A. Kumar, "Utilizing Machine Learning Techniques for Worst-Case Execution Time Estimation on GPU Architectures," in IEEE Access, vol. 12, pp. 41464-41478, 2024, doi: 10.1109/ACCESS.2024.3379018. [paper] [code]

  2. V. Kumar, B. Ranjbar and A. Kumar, "ESOMICS: ML-Based Timing Behavior Analysis for Efficient Mixed-Criticality System Design," in IEEE Access, vol. 12, pp. 67013-67024, 2024, doi: 10.1109/ACCESS.2024.3396225. [paper] [code]

Conference Proceedings

  1. V. Kumar, B. Ranjbar and A. Kumar, "Motivating the Use of Machine-Learning for Improving Timing Behaviour of Embedded Mixed-Criticality Systems," 2024 Design, Automation & Test in Europe Conference & Exhibition (DATE), Valencia, Spain, 2024 [paper]

  2. V. Kumar, "An integrated approach of Genetic Algorithm and Machine Learning for generation of Worst-Case Data for Real-Time Systems," 2022 IEEE/ACM 26th International Symposium on Distributed Simulation and Real Time Applications (DS-RT), Alès, France, 2022, pp. 87-95, doi: 10.1109/DS-RT55542.2022.9932054. [paper]

  3. V. Kumar, "Estimation of an Early WCET Using Different Machine Learning Approaches". In: Barolli, L. (eds) Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 3PGCIC 2022. Lecture Notes in Networks and Systems, vol 571. Springer, Cham. https://doi.org/10.1007/978-3-031-19945-5_30. [paper]

  4. V. Kumar, "Deep Neural Network Approach to Estimate Early Worst-Case Execution Time," 2021 IEEE/AIAA 40th Digital Avionics Systems Conference (DASC), San Antonio, TX, USA, 2021, pp. 1-8, doi: 10.1109/DASC52595.2021.9594326. [ paper].