| 183 | -- | 184 | Mitra Nasri, Sanjoy K. Baruah. Guest editorial: a roadmap towards learning-enabled and learning-assisted real-time systems |
| 185 | -- | 236 | 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 |
| 237 | -- | 252 | Giorgio Buttazzo. Toward predictable AI-based real-time systems |
| 253 | -- | 258 | Yasmina Abdeddaïm, Mourad Dridi, Joshua Dumont. Research directions for real-time implementation of AI algorithms |
| 259 | -- | 267 | Seunghoon Lee, Woosung Kang, Marko Bertogna, Hoon Sung Chwa, JinKyu Lee. Timing guarantees for inference of AI models in embedded systems |
| 268 | -- | 274 | Benjamin Lesage, Adrien Gauffriau, Claire Pagetti, Nicolas Valot. Challenges of neural network accelerators for aeronautics - position paper |
| 275 | -- | 280 | Joshua Bakita, James H. Anderson. The advantage of the GPU as a real-time AI accelerator |
| 281 | -- | 287 | Michael Yuhas, Arvind Easwaran. Toward state-aware scheduling of machine-learning workloads |
| 288 | -- | 293 | Mirco Theile, Binqi Sun, Marco Caccamo. Position paper: deep reinforcement learning for real-time resource management |
| 294 | -- | 299 | Daniel Casini. To MILP or not to MILP? On AI techniques for the design and optimization of real-time systems |
| 300 | -- | 305 | Abderaouf Nassim Amalou, Isabelle Puaut. Using machine learning for timing analysis: where do we stand? |
| 306 | -- | 310 | Junjie Shi, Kuan-Hsun Chen. Shielded reinforcement learning for fault-tolerant scheduling in real-time systems |
| 311 | -- | 319 | Daniel Kuhse, Harun Teper, Christian Hakert, Jian-Jia Chen. Timely ML |
| 320 | -- | 325 | Zhishan Guo. When machine learning and neural networks marry real-time scheduling |