2 citations · 2 across the 2 of their papers we have counts for
5 papers
Efficient Expert-Parallel Communication on PCIe-Connected Consumer GPUs
Jaehwan Lee, Sangmin Lee, Chaewon Kim +2
Expert parallelism (EP) enables inference of large Mixture-of-Experts (MoE) models by placing their experts across multiple GPUs, but requires substantial communication between GPU…
HetCCL: Accelerating LLM Training with Heterogeneous GPUs
Heehoon Kim, Jaehwan Lee, Taejeoung Kim +6
The rapid growth of large language models is driving organizations to expand their GPU clusters, often with GPUs from multiple vendors. However, current deep learning frameworks la…
Integrating Spatial and Frequency Information for Under-Display Camera Image Restoration
Kyusu Ahn, Jinpyo Kim, Chanwoo Park +2
Under-Display Camera (UDC) houses a digital camera lens under a display panel. However, UDC introduces complex degradations such as noise, blur, decrease in transmittance, and flar…
UDC-VIT: A Real-World Video Dataset for Under-Display Cameras
Kyusu Ahn, JiSoo Kim, Sangik Lee +4
Even though an Under-Display Camera (UDC) is an advanced imaging system, the display panel significantly degrades captured images or videos, introducing low transmittance, blur, no…
SEDD: Scalable and Efficient Dataset Deduplication with GPUs
Youngjun Son, Chaewon Kim, Jaejin Lee
Dataset deduplication is widely recognized as a crucial preprocessing step that enhances data quality and improves the performance of large language models. A commonly used method…