4 papers
Efficient Few-Shot Learning for Edge AI via Knowledge Distillation on MobileViT
Shuhei Tsuyuki, Reda Bensaid, Jérémy Morlier +4
Efficient and adaptable deep learning models are an important area of deep learning research, driven by the need for highly efficient models on edge devices. Few-shot learning enab…
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.…
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…
PEFSL: A deployment Pipeline for Embedded Few-Shot Learning on a FPGA SoC
Lucas Grativol Ribeiro, Lubin Gauthier, Mathieu Leonardon +5
This paper tackles the challenges of implementing few-shot learning on embedded systems, specifically FPGA SoCs, a vital approach for adapting to diverse classification tasks, espe…