3 papers
cs.CV2026
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…
cs.LG2026
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.…
cs.LG2023
DeepGEMM: Accelerated Ultra Low-Precision Inference on CPU Architectures using Lookup Tables
Darshan C. Ganji, Saad Ashfaq, Ehsan Saboori +6
A lot of recent progress has been made in ultra low-bit quantization, promising significant improvements in latency, memory footprint and energy consumption on edge devices. Quanti…