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ORCIDReal-Time Systems, Volume 61
Volume 61, Number 1, March 2025
- Luís Almeida:
Editorial - News from the 61st volume. 1-3 - Bruno Gaujal, Alain Girault, Stéphan Plassart
:
An MDP-based solution for the energy minimization of non-clairvoyant hard real-time systems. 4-52 - Shashank Jadhav, Heiko Falk:
Compiler-level DMA-aware multi-objective dynamic SPM allocation. 53-117 - Kyonghwan Yoon, Eunjin Jeong, Woosuk Kang, Jonghyun Choe, Soonhoi Ha:
Worst case response time analysis for completely fair scheduling in Linux systems. 118-158 - Richard Garreau, Frédéric Ridouard, Henri Bauer, Pascal Richard:
Buffer dimensioning per frames in packet networks. 159-182
Volume 61, Number 2, June 2025
Special Issue: A Roadmap Towards Learning-Enabled and Learning-Assisted Real-Time Systems
- Mitra Nasri, Sanjoy K. Baruah:
Guest editorial: a roadmap towards learning-enabled and learning-assisted real-time systems. 183-184 - Tarek F. Abdelzaher, Yigong Hu, Denizhan Kara, Tomoyoshi Kimura, Ashitabh Misra, Vishakha Ramani, Olivier Tardieu, Tianshi Wang, Maggie B. Wigness, Alaa Youssef:
The bottlenecks of AI: challenges for embedded and real-time research in a data-centric age. 185-236 - Giorgio Buttazzo:
Toward predictable AI-based real-time systems. 237-252 - Yasmina Abdeddaïm, Mourad Dridi, Joshua Dumont:
Research directions for real-time implementation of AI algorithms. 253-258 - Seunghoon Lee, Woosung Kang, Marko Bertogna, Hoon Sung Chwa, Jinkyu Lee:
Timing guarantees for inference of AI models in embedded systems. 259-267 - Benjamin Lesage, Adrien Gauffriau, Claire Pagetti, Nicolas Valot:
Challenges of neural network accelerators for aeronautics - position paper. 268-274 - Joshua Bakita, James H. Anderson:
The advantage of the GPU as a real-time AI accelerator. 275-280 - Michael Yuhas, Arvind Easwaran:
Toward state-aware scheduling of machine-learning workloads. 281-287 - Mirco Theile, Binqi Sun, Marco Caccamo:
Position paper: deep reinforcement learning for real-time resource management. 288-293 - Daniel Casini:
To MILP or not to MILP? On AI techniques for the design and optimization of real-time systems. 294-299 - Abderaouf Nassim Amalou, Isabelle Puaut:
Using machine learning for timing analysis: where do we stand? 300-305 - Junjie Shi, Kuan-Hsun Chen:
Shielded reinforcement learning for fault-tolerant scheduling in real-time systems. 306-310 - Daniel Kuhse, Harun Teper, Christian Hakert, Jian-Jia Chen:
Timely ML. 311-319 - Zhishan Guo:
When machine learning and neural networks marry real-time scheduling. 320-325
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