| Optimization of global production scheduling with deep reinforcement learning B Waschneck, A Reichstaller, L Belzner, T Altenmüller, T Bauernhansl, ... Procedia Cirp 72, 1264-1269, 2018 | 346 | 2018 |
| Deep reinforcement learning for semiconductor production scheduling B Waschneck, A Reichstaller, L Belzner, T Altenmüller, T Bauernhansl, ... 2018 29th annual SEMI advanced semiconductor manufacturing conference (ASMC …, 2018 | 139 | 2018 |
| Production Scheduling in Complex Job Shops from an Industry 4.0 Perspective: A Review and Challenges in the Semiconductor Industry. B Waschneck, T Altenmüller, T Bauernhansl, A Kyek SAMI@ iKNOW 1793, 2016 | 101 | 2016 |
| Small-footprint keyword spotting on raw audio data with sinc-convolutions S Mittermaier, L Kürzinger, B Waschneck, G Rigoll ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020 | 70 | 2020 |
| Reinforcement learning for an intelligent and autonomous production control of complex job-shops under time constraints T Altenmüller, T Stüker, B Waschneck, A Kuhnle, G Lanza Production Engineering, 1-10, 2020 | 58 | 2020 |
| Opportunistic maintenance scheduling with deep reinforcement learning Alexander Valet, Thomas Altenmüller, Bernd Waschneck, Marvin Carl May ... Journal of Manufacturing Systems 64, 518-534, 2022 | 34 | 2022 |
| Autonome Entscheidungsfindung in der Produktionssteuerung komplexer Werkstattfertigungen B Waschneck Stuttgart: Fraunhofer Verlag, 2020 | 10 | 2020 |
| dCSR: a memory-efficient sparse matrix representation for parallel neural network inference E Trommer, B Waschneck, A Kumar 2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD), 1-9, 2021 | 7 | 2021 |
| Ai-driven performance modeling for AI inference workloads M Sponner, B Waschneck, A Kumar Electronics 11 (15), 2316, 2022 | 6 | 2022 |
| Convolutional neural networks quantization with double-stage squeeze-and-threshold B Wu, B Waschneck, CG Mayr International Journal of Neural Systems 32 (12), 2250051, 2022 | 5 | 2022 |
| Compiler toolchains for deep learning workloads on embedded platforms M Sponner, B Waschneck, A Kumar arXiv preprint arXiv:2104.04576, 2021 | 5 | 2021 |
| Squeeze-and-threshold based quantization for low-precision neural networks B Wu, B Waschneck, C Mayr International Conference on Engineering Applications of Neural Networks, 232-243, 2021 | 4 | 2021 |
| Unified frontend and backend industrie 4.0 roadmap for semiconductor manufacturing B Waschneck, LWF Brian, KCW Benny, C Rippler, G Schmid Proceedings of the 2017 International Conference on Knowledge Technologies …, 2017 | 4 | 2017 |
| Toward combined transport and optical studies of the 0.7‐anomaly in a quantum point contact E Schubert, J Heyder, F Bauer, B Waschneck, W Stumpf, W Wegscheider, ... physica status solidi (b) 251 (9), 1931-1937, 2014 | 4 | 2014 |
| Combining gradients and probabilities for heterogeneous approximation of neural networks E Trommer, B Waschneck, A Kumar Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided …, 2022 | 2 | 2022 |
| Case Study on Operator Compliance to Scheduling Decisions in Semiconductor Manufacturing B Waschneck, T Altenmüller, T Bauernhansl, A Kyek 14th IEEE International Conference on Automation Science and Engineering (CASE), 2018 | 2 | 2018 |
| Concept and Possible Application of an Automated Framework to Influence Production Dispatch Based on Supply Chain Events D Bauer, F Maier, T Bauernhansl, B Waschneck, T Ponsignon, D Gürster, ... 7th IESM Conference, 2017 | 2 | 2017 |
| Automating application-driven customization of ASIPs: A survey E Hussein, B Waschneck, C Mayr Journal of Systems Architecture, 103080, 2024 | 1 | 2024 |
| 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 | 1 | 2023 |
| High-Throughput Approximate Multiplication Models in PyTorch E Trommer, B Waschneck, A Kumar 2023 26th International Symposium on Design and Diagnostics of Electronic …, 2023 | 1 | 2023 |
