5 papers
Contrastive Learning for Seismic Horizon Tracking with Domain-Specific Priors
Alexandre Thouvenot, Lionel Boillot, Vincent Gripon
Unsupervised 3D seismic horizon tracking faces a key limitation: signal-based propagators provide accurate trace-level alignment but often fail near faults, whereas texture-driven…
MONET: Modeling and Optimization of neural NEtwork Training from Edge to Data Centers
Jérémy Morlier, Robin Geens, Stef Cuyckens +4
While hardware-software co-design has significantly improved the efficiency of neural network inference, modeling the training phase remains a critical yet underexplored challenge.…
Bit-Width-Aware Design Environment for Few-Shot Learning on Edge AI Hardware
R. Kanda, H. L. Blevec, N. Onizawa +3
In this study, we propose an implementation methodology of real-time few-shot learning on tiny FPGA SoCs such as the PYNQ-Z1 board with arbitrary fixed-point bit-widths. Tensil-bas…
Design Environment of Quantization-Aware Edge AI Hardware for Few-Shot Learning
R. Kanda, N. Onizawa, M. Leonardon +2
This study aims to ensure consistency in accuracy throughout the entire design flow in the implementation of edge AI hardware for few-shot learning, by implementing fixed-point dat…
Input Resolution Downsizing as a Compression Technique for Vision Deep Learning Systems
Jeremy Morlier, Mathieu Leonardon, Vincent Gripon
Model compression is a critical area of research in deep learning, in particular in vision, driven by the need to lighten models memory or computational footprints. While numerous…