most citedExploring the Trade-Offs: Quantization Methods, Task Difficulty, and Model Size in Large Language Models From Edge to Giant

2 citations · 2 across the 2 of their papers we have counts for

collaborators

3 papers

cs.DC2026

Lightweight and Resource-Efficient Perception for Robotic Guide Dogs

Jinse Kwon, Yoojin Lim, Choonghan Lee +3

Robotic guide dogs should understand their surroundings, objects, and potential risks. Prior research has focused on raw sensor data from cameras and 2D or 3D LiDAR, which precisel…

cs.LG2024

MLTuner: Efficient Code Tuning via Multi-Level Machine Learning Models

JooHyoung Cha, Munyoung Lee, Jinse Kwon +3

The increasing complexity of deep learning models necessitates specialized hardware and software optimizations, particularly for deep learning accelerators. Existing autotuning met…

cs.CL2024★ 2 cited

Exploring the Trade-Offs: Quantization Methods, Task Difficulty, and Model Size in Large Language Models From Edge to Giant

Jemin Lee, Sihyeong Park, Jinse Kwon +2

Quantization has gained attention as a promising solution for the cost-effective deployment of large and small language models. However, most prior work has been limited to perplex…