21 citations · 40 across the 11 of their papers we have counts for
5 papers · 1 filter
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…
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…
Mixed Non-linear Quantization for Vision Transformers
Gihwan Kim, Jemin Lee, Sihyeong Park +2
The majority of quantization methods have been proposed to reduce the model size of Vision Transformers, yet most of them have overlooked the quantization of non-linear operations.…
LLMem: Estimating GPU Memory Usage for Fine-Tuning Pre-Trained LLMs
Taeho Kim, Yanming Wang, Vatshank Chaturvedi +4
Fine-tuning pre-trained large language models (LLMs) with limited hardware presents challenges due to GPU memory constraints. Various distributed fine-tuning methods have been prop…
Visual Preference Inference: An Image Sequence-Based Preference Reasoning in Tabletop Object Manipulation
Joonhyung Lee, Sangbeom Park, Yongin Kwon +3
In robotic object manipulation, human preferences can often be influenced by the visual attributes of objects, such as color and shape. These properties play a crucial role in oper…