21 citations · 40 across the 11 of their papers we have counts for
4 papers · 1 filter
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…
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…
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…