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Nathan Laubeuf
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2020 – today
- 2023
- [j4]Iman Dadras
, Giuseppe Maria Sarda
AIMC Modeling and Parameter Tuning for Layer-Wise Optimal Operating Point in DNN Inference. IEEE Access 11: 87189-87199 (2023) - 2022
- [j3]Nathan Laubeuf
Dynamic Quantization Range Control for Analog-in-Memory Neural Networks Acceleration. ACM Trans. Design Autom. Electr. Syst. 27(5): 46:1-46:21 (2022) - [c4]Nitish Satya Murthy, Peter Vrancx, Nathan Laubeuf, Peter Debacker, Francky Catthoor, Marian Verhelst
Learn to Learn on Chip: Hardware-aware Meta-learning for Quantized Few-shot Learning at the Edge. SEC 2022: 14-25 - [c3]Nathan Laubeuf:
Analog Compute in Memory and Breaking Digital Number Representations. VLSI-SoC 2022: 1-2 - 2021
- [c2]Jonas Doevenspeck, Peter Vrancx, Nathan Laubeuf, Arindam Mallik, Peter Debacker, Diederik Verkest, Rudy Lauwereins, Wim Dehaene:
Noise tolerant ternary weight deep neural networks for analog in-memory inference. IJCNN 2021: 1-8 - [c1]Debjyoti Bhattacharjee
Design-Technology Space Exploration for Energy Efficient AiMC-Based Inference Acceleration. ISCAS 2021: 1-5
2010 – 2019
- 2019
- [j2]Laurent Dejace, Nathan Laubeuf, Ivan Furfaro
Gallium-Based Thin Films for Wearable Human Motion Sensors. Adv. Intell. Syst. 1(5): 1900079 (2019) - [j1]Laurent Dejace, Nathan Laubeuf, Ivan Furfaro, Stéphanie P. Lacour
Gallium-Based Thin Films for Wearable Human Motion Sensors. Adv. Intell. Syst. 1(5): 1970050 (2019) - [i1]Bram-Ernst Verhoef, Nathan Laubeuf, Stefan Cosemans, Peter Debacker, Ioannis A. Papistas, Arindam Mallik, Diederik Verkest:
FQ-Conv: Fully Quantized Convolution for Efficient and Accurate Inference. CoRR abs/1912.09356 (2019)
Coauthor Index
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