| UltraTrail: A Configurable Ultralow-Power TC-ResNet AI Accelerator for Efficient Keyword Spotting PP Bernardo, C Gerum, A Frischknecht, K Lübeck, O Bringmann IEEE Transactions on Computer-Aided Design of Integrated Circuits and …, 2020 | 32 | 2020 |
| Hardware Accelerator and Neural Network Co-Optimization for Ultra-Low-Power Audio Processing Devices C Gerum, A Frischknecht, T Hald, PP Bernardo, K Lübeck, O Bringmann arXiv preprint arXiv:2209.03807, 2022 | 6 | 2022 |
| A Heterogeneous and Reconfigurable Embedded Architecture for Energy-Efficient Execution of Convolutional Neural Networks K Lübeck, O Bringmann International Conference on Architecture of Computing Systems, 267-280, 2019 | 6 | 2019 |
| Work-in-Progress: Ultra-fast yet Accurate Performance Prediction for Deep Neural Network Accelerators K Lübeck, ALF Jung, F Wedlich, O Bringmann 2022 International Conference on Compilers, Architecture, and Synthesis for …, 2022 | 2 | 2022 |
| Hardware Accelerator and Neural Network Co-Optimization for Ultra-Low-Power Audio Processing Devices G Christoph, F Adrian, H Tobias, PP Bernardo, K Lübeck, B Oliver 2022 25th Euromicro Conference on Digital System Design (DSD), 365-369, 2022 | 2 | 2022 |
| APPEL-AGILA ProPErty and Dependency Description Language C Grimm, F Wawrzik, ALF Jung, K Lübeck, S Post, J Koch, O Bringmann MBMV 2021; 24th Workshop, 1-11, 2021 | 2 | 2021 |
| Neues Konzept zur Steigerung der Zuverlässigkeit einer ARM-basierten Prozessorarchitektur unter Verwendung eines CGRAs. K Lübeck, D Morgenstern, T Schweizer, D Peterson, W Rosenstiel, ... MBMV, 46-58, 2016 | 1 | 2016 |
| It's all about PR--Smart Benchmarking AI Accelerators using Performance Representatives ALF Jung, J Steinmetz, J Gietz, K Lübeck, O Bringmann arXiv preprint arXiv:2406.08330, 2024 | | 2024 |
| Using the Abstract Computer Architecture Description Language to Model AI Hardware Accelerators MM Müller, ARM Borst, K Lübeck, ALF Jung, O Bringmann arXiv preprint arXiv:2402.00069, 2024 | | 2024 |
