| Adapting Neural Networks at Runtime: Current Trends in At-Runtime Optimizations for Deep Learning M Sponner, B Waschneck, A Kumar ACM Computing Surveys 56 (10), 1-40, 2024 | 7 | 2024 |
| Compiler toolchains for deep learning workloads on embedded platforms M Sponner, B Waschneck, A Kumar arXiv preprint arXiv:2104.04576, 2021 | 7 | 2021 |
| AI-driven performance modeling for AI inference workloads M Sponner, B Waschneck, A Kumar Electronics 11 (15), 2316, 2022 | 6 | 2022 |
| Temporal Patience: Efficient Adaptive Deep Learning for Embedded Radar Data Processing M Sponner, J Ott, L Servadei, B Waschneck, R Wille, A Kumar arXiv preprint arXiv:2309.05686, 2023 | 5 | 2023 |
| Harnessing Temporal Information for Efficient Edge AI M Sponner, L Servadei, B Waschneck, R Wille, A Kumar 2024 9th International Conference on Fog and Mobile Edge Computing (FMEC), 5-13, 2024 | 2 | 2024 |
| Temporal Decisions: Leveraging Temporal Correlation for Efficient Decisions in Early Exit Neural Networks M Sponner, L Servadei, B Waschneck, R Wille, A Kumar arXiv preprint arXiv:2403.07958, 2024 | 2 | 2024 |
| Efficient Post-Training Augmentation for Adaptive Inference in Heterogeneous and Distributed IoT Environments M Sponner, L Servadei, B Waschneck, R Wille, A Kumar arXiv preprint arXiv:2403.07957, 2024 | 2 | 2024 |
| PEAX-A Model Augmentation Framework for Adaptive Techniques and Embedded Applications M Sponner, L Servadei, B Waschneck, R Wille, A Kumar | | 2025 |
| Leveraging Temporal Patterns: Automated Augmentation to Create Temporal Early Exit Networks for Efficient Edge AI M Sponner, L Servadei, B Waschneck, R Wille, A Kumar IEEE Access, 2024 | | 2024 |
| Early-exit neural networks for radar processing M Sponner, L Servadei, B Waschneck US Patent App. 18/584,629, 2024 | | 2024 |
Dr. Bernd WaschneckInfineon Technologies AG在 infineon.com 的电子邮件经过验证
