1 citations · 1 across the 1 of their papers we have counts for
2 papers
cs.DC2025★ 1 cited
TT-Edge: A Hardware-Software Co-Design for Energy-Efficient Tensor-Train Decomposition on Edge AI
Hyunseok Kwak, Kyeongwon Lee, Kyeongpil Min +2
The growing demands of distributed learning on resource constrained edge devices underscore the importance of efficient on device model compression. Tensor Train Decomposition (TTD…
cs.CV2025
LoRA-Edge: Tensor-Train-Assisted LoRA for Practical CNN Fine-Tuning on Edge Devices
Hyunseok Kwak, Kyeongwon Lee, Jae-Jin Lee +1
On-device fine-tuning of CNNs is essential to withstand domain shift in edge applications such as Human Activity Recognition (HAR), yet full fine-tuning is infeasible under strict…