关注
Dr. Bernd Waschneck
标题
引用次数
引用次数
年份
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
3662018
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
1452018
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, 109, 2016
1132016
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
742020
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
712020
Opportunistic maintenance scheduling with deep reinforcement learning
Alexander Valet, Thomas Altenmüller, Bernd Waschneck, Marvin Carl May ...
Journal of Manufacturing Systems 64, 518-534, 2022
372022
Autonome Entscheidungsfindung in der Produktionssteuerung komplexer Werkstattfertigungen
B Waschneck
Stuttgart: Fraunhofer Verlag, 2020
112020
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
102021
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
62022
Compiler toolchains for deep learning workloads on embedded platforms
M Sponner, B Waschneck, A Kumar
arXiv preprint arXiv:2104.04576, 2021
62021
AI-driven performance modeling for AI inference workloads
M Sponner, B Waschneck, A Kumar
Electronics 11 (15), 2316, 2022
52022
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
52021
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
42017
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
42014
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
22022
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
22018
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
22017
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
12024
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
12024
Automating application-driven customization of ASIPs: A survey
E Hussein, B Waschneck, C Mayr
Journal of Systems Architecture, 103080, 2024
12024
系统目前无法执行此操作,请稍后再试。
文章 1–20