Training Machine Learning models at the Edge: A Survey

AR Khouas, MR Bouadjenek, H Hacid… - arXiv preprint arXiv …, 2024 - arxiv.org
Edge Computing (EC) has gained significant traction in recent years, promising enhanced
efficiency by integrating Artificial Intelligence (AI) capabilities at the edge. While the focus …

Accelerating On-Device DNN Training Workloads via Runtime Convergence Monitor

S Choi, J Shin, LS Kim - IEEE Transactions on Computer-Aided …, 2022 - ieeexplore.ieee.org
With the growing demand for processing deep learning applications on edge devices, on-
device DNN training has become a major workload to execute a variety of vision tasks suited …

Poster: Fast On-Device Adaptation with Approximate Forward Training

M Shim, Y Lee - Proceedings of the 22nd Annual International …, 2024 - dl.acm.org
Enabling real-time machine learning (ML) model adaptation to previously unseen, but highly
specific contexts and environments can vastly extend the capability of mobile and ubiquitous …