4 citations · 4 across the 3 of their papers we have counts for
4 papers
FlowTT: Exploiting Computation Flow Reuse in Irregular Tensor-Train Embedding
Jongmin Seok, Chae Eun Rhee
Tensor-Train (TT) decomposition effectively compresses large embedding tables in recommendation models, but TT-based embedding lookup remains inefficient because partially shared c…
CC-4DGS: Computational Deformation and Point-Cloud Compression for Storage-Efficient Dynamic Gaussian Splatting
Kyungdae Park, Chae Eun Rhee
Dynamic four-dimensional (4D) Gaussian Splatting has emerged as a powerful explicit representation for high-quality view synthesis, yet existing methods still require tens to hundr…
ProactivePIM: Accelerating Weight-Sharing Embedding Layer with PIM for Scalable Recommendation System
Youngsuk Kim, Junghwan Lim, Hyuk-Jae Lee +1
Although deep learning-based personalized recommendation systems provide qualified recommendations, they strain data center resources. The main bottleneck is the embedding layer, w…
Wireless VR/Haptic Open Platform for Multimodal Teleoperation
Tae Hun Jung, Hanju Yoo, Yuna Jin +2
With emerging trends in the fifth generation and robotics, the Internet of Skills will enable us to deliver skills or expertise anywhere over the Internet. In this paper, we propos…