| nr |
titel |
auteur |
tijdschrift |
jaar |
jaarg. |
afl. |
pagina('s) |
type |
| 1 |
Challenges of neural network accelerators for aeronautics—position paper
|
Lesage, Benjamin
|
|
|
61 |
2 |
p. 268-274
|
artikel
|
| 2 |
Guest editorial: a roadmap towards learning-enabled and learning-assisted real-time systems
|
Nasri, Mitra
|
|
|
61 |
2 |
p. 183-184
|
artikel
|
| 3 |
Position paper: deep reinforcement learning for real-time resource management
|
Theile, Mirco
|
|
|
61 |
2 |
p. 288-293
|
artikel
|
| 4 |
Research directions for real-time implementation of AI algorithms
|
Abdeddaïm, Yasmina
|
|
|
61 |
2 |
p. 253-258
|
artikel
|
| 5 |
Shielded reinforcement learning for fault-tolerant scheduling in real-time systems
|
Shi, Junjie
|
|
|
61 |
2 |
p. 306-310
|
artikel
|
| 6 |
The advantage of the GPU as a real-time AI accelerator
|
Bakita, Joshua
|
|
|
61 |
2 |
p. 275-280
|
artikel
|
| 7 |
The bottlenecks of AI: challenges for embedded and real-time research in a data-centric age
|
Abdelzaher, Tarek
|
|
|
61 |
2 |
p. 185-236
|
artikel
|
| 8 |
Timely ML
|
Kuhse, Daniel
|
|
|
61 |
2 |
p. 311-319
|
artikel
|
| 9 |
Timing guarantees for inference of AI models in embedded systems
|
Lee, Seunghoon
|
|
|
61 |
2 |
p. 259-267
|
artikel
|
| 10 |
To MILP or not to MILP? On AI techniques for the design and optimization of real-time systems
|
Casini, Daniel
|
|
|
61 |
2 |
p. 294-299
|
artikel
|
| 11 |
Toward predictable AI-based real-time systems
|
Buttazzo, Giorgio
|
|
|
61 |
2 |
p. 237-252
|
artikel
|
| 12 |
Toward state-aware scheduling of machine-learning workloads
|
Yuhas, Michael
|
|
|
61 |
2 |
p. 281-287
|
artikel
|
| 13 |
Using machine learning for timing analysis: where do we stand?
|
Amalou, Abderaouf Nassim
|
|
|
61 |
2 |
p. 300-305
|
artikel
|
| 14 |
When machine learning and neural networks marry real-time scheduling
|
Guo, Zhishan
|
|
|
61 |
2 |
p. 320-325
|
artikel
|
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