21 citations · 28 across the 9 of their papers we have counts for
10 papers
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…
QFlash: Bridging Quantization and Memory Efficiency in Vision Transformer Attention
Sehyeon Oh, Yongin Kwon, Jemin Lee
FlashAttention improves efficiency through tiling, but its online softmax still relies on floating-point arithmetic for numerical stability, making full quantization difficult. We…
QuantuneV2: Compiler-Based Local Metric-Driven Mixed Precision Quantization for Practical Embedded AI Applications
Jeongseok Kim, Jemin Lee, Yongin Kwon +1
Mixed-precision quantization methods have been proposed to reduce model size while minimizing accuracy degradation. However, existing studies require retraining and do not consider…
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.…
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…
Q-HyViT: Post-Training Quantization of Hybrid Vision Transformers with Bridge Block Reconstruction for IoT Systems
Jemin Lee, Yongin Kwon, Sihyeong Park +3
Recently, vision transformers (ViTs) have superseded convolutional neural networks in numerous applications, including classification, detection, and segmentation. However, the hig…
CPrune: Compiler-Informed Model Pruning for Efficient Target-Aware DNN Execution
Taeho Kim, Yongin Kwon, Jemin Lee +1
Mobile devices run deep learning models for various purposes, such as image classification and speech recognition. Due to the resource constraints of mobile devices, researchers ha…
Quantune: Post-training Quantization of Convolutional Neural Networks using Extreme Gradient Boosting for Fast Deployment
Jemin Lee, Misun Yu, Yongin Kwon +1
To adopt convolutional neural networks (CNN) for a range of resource-constrained targets, it is necessary to compress the CNN models by performing quantization, whereby precision r…