Fast dynamic execution offloading for efficient mobile cloud computing. - Seungjun Yang; Yongin Kwon; Yeongpil Cho; Hayoon Yi; Donghyun Kwon; Jonghee M. Youn; Yunheung Paek (PerCom, 2013)

Mantis: Automatic Performance Prediction for Smartphone Applications. - Yongin Kwon; Sangmin Lee; Hayoon Yi; Donghyun Kwon; Seungjun Yang; Byung-Gon Chun; Ling Huang 001; Petros Maniatis; Mayur Naik; Yunheung Paek (USENIX ATC, 2013)

CMcloud: Cloud Platform for Cost-Effective Offloading of Mobile Applications. - Dongju Chae; Jihun Kim 002; Jangwoo Kim; Jong Kim 001; Seungjun Yang; Yeongpil Cho; Yongin Kwon; Yunheung Paek (CCGRID, 2014)

Techniques to Minimize State Transfer Costs for Dynamic Execution Offloading in Mobile Cloud Computing. - Seungjun Yang; Donghyun Kwon; Hayoon Yi; Yeongpil Cho; Yongin Kwon; Yunheung Paek (IEEE Trans. Mob. Comput., 2014)

Mantis: Efficient Predictions of Execution Time, Energy Usage, Memory Usage and Network Usage on Smart Mobile Devices. - Yongin Kwon; Sangmin Lee; Hayoon Yi; Donghyun Kwon; Seungjun Yang; Byung-Gon Chun; Ling Huang 001; Petros Maniatis; Mayur Naik; Yunheung Paek (IEEE Trans. Mob. Comput., 2015)

Precise execution offloading for applications with dynamic behavior in mobile cloud computing. - Yongin Kwon; Hayoon Yi; Donghyun Kwon; Seungjun Yang; Yeongpil Cho; Yunheung Paek (Pervasive Mob. Comput., 2016)

Microarchitecture-Aware Code Generation for Deep Learning on Single-ISA Heterogeneous Multi-Core Mobile Processors. - Junmo Park; Yongin Kwon; Yongjun Park 001; Dongsuk Jeon (IEEE Access, 2019)

CPrune: Compiler-Informed Model Pruning for Efficient Target-Aware DNN Execution. - Taeho Kim 002; Yongin Kwon; Jemin Lee; Taeho Kim 001; Sangtae Ha (ECCV (20), 2022)

Quantune: Post-training quantization of convolutional neural networks using extreme gradient boosting for fast deployment. - Jemin Lee; Misun Yu; Yongin Kwon; Taeho Kim 001 (Future Gener. Comput. Syst., 2022)

Q-HyViT: Post-Training Quantization for Hybrid Vision Transformer with Bridge Block Reconstruction. - Jemin Lee; Yongin Kwon; Jeman Park 002; Misun Yu; Hwanjun Song (CoRR, 2023)

Tensor slicing and optimization for multicore NPUs. - Rafael C. F. Sousa; Márcio Machado Pereira; Yongin Kwon; Taeho Kim; Namsoon Jung; Chang Soo Kim; Michael Frank 008; Guido Araujo (J. Parallel Distributed Comput., 2023)

Mixed Non-linear Quantization for Vision Transformers. - Gihwan Kim; Jemin Lee; Sihyeong Park; Yongin Kwon; Hyungshin Kim (ECCV Workshops (11), 2024)

LLMem: Estimating GPU Memory Usage for Fine-Tuning Pre-Trained LLMs. - Taeho Kim; Yanming Wang; Vatshank Chaturvedi; Lokesh Gupta; Seyeon Kim; Yongin Kwon; Sangtae Ha (IJCAI, 2024)

Visual Preference Inference: An Image Sequence-Based Preference Reasoning in Tabletop Object Manipulation. - Joonhyung Lee; Sangbeom Park; Yongin Kwon; Jemin Lee; Minwook Ahn; Sungjoon Choi (IROS, 2024)

A Comprehensive Evaluation of Quantized Instruction-Tuned Large Language Models: An Experimental Analysis up to 405B. - Jemin Lee; Sihyeong Park; Jinse Kwon; Jihun Oh; Yongin Kwon (CoRR, 2024)

ML2Tuner: Efficient Code Tuning via Multi-Level Machine Learning Models. - JooHyoung Cha; Munyoung Lee; Jinse Kwon; Jubin Lee; Jemin Lee; Yongin Kwon (CoRR, 2024)

Q-HyViT: Post-Training Quantization of Hybrid Vision Transformers With Bridge Block Reconstruction for IoT Systems. - Jemin Lee; Yongin Kwon; Sihyeong Park; Misun Yu; Jeman Park 002; Hwanjun Song (IEEE Internet Things J., 2024)

Flexible Uber Fetch Shader Architecture for Fluid Gaming Experience. - Junmo Park; Zhenhong Liu; Sangwon Park; Yongin Kwon; Wilson Fung; Donghee Han (ICCE, 2025)

Multi-level Machine Learning-Guided Autotuning for Efficient Code Generation on a Deep Learning Accelerator. - JooHyoung Cha; Munyoung Lee; Jinse Kwon; Jemin Lee; Yongin Kwon (LCTES, 2025)

QuantuneV2: Compiler-based local metric-driven mixed precision quantization for practical embedded AI applications. - Jeongseok Kim; Jemin Lee; Yongin Kwon; Daeyoung Kim 001 (Future Gener. Comput. Syst., 2025)

QuantuneV2: Compiler-Based Local Metric-Driven Mixed Precision Quantization for Practical Embedded AI Applications. - Jeongseok Kim; Jemin Lee; Yongin Kwon; Daeyoung Kim (CoRR, 2025)