Research

Lab

Dependable ComputingLab

Prof. Kyoungwoo Lee

02-2123-7429

kyoungwoo.lee@yonsei.ac.kr

Engineering Hall 4, Room D809

Lab Introduction

The dependable computing lab conducts research to analyze the vulnerabilities of the system against various faults including soft errors, caused by external energy sources, based on the fault injection simulation and quantitative vulnerability estimation, and to enhance the reliability of computer systems against the faults, based on the collaboration with Arizona State University. In addition, our research topics also include AI convergence research including machine learning-based early predictions for patent ductus arteriosus (PDA) and intraventricular hemorrhage (IVH), developing customized childcare services for safe childcare environments, Establishing a self-tracking system for pediatric patients with daytime urinary frequency syndrome (DUFS), based on the collaboration with Severance children's hospital and human-computer interaction (HCI) research team, and the optimization for deep learning accelerators. Our researches appear in international conferences and journals including DAC, ICCAD, DATE, TECS, ICCD, NAU, BIBM.

Research field
Soft error (transient fault) Reliability Fault tolerance IoT-based healthcare Machine learning-based healthcare Deep learning accelerator
Representative Papers
  • So, H., Didehban, M., Jung, J., Shrivastava, A., & Lee, K. (2021, February). CHITIN: A Comprehensive In-thread Instruction Replication Technique Against Transient Faults. In 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE) (pp. 1440-1445). IEEE.
  • Lee, S., Moon, J., Shin, S. C., Hwang, J. H., Lee, K., & Lee, Y. S. (2021). Continuous body impedance measurement to detect bladder volume changes during urodynamic study: A prospective study in pediatric patients. Neurourology and Urodynamics, 40(1), 421-427.
  • Dave, S., Kim, Y., Avancha, S., Lee, K., & Shrivastava, A. (2019). DMazeRunner: Executing perfectly nested loops on dataflow accelerators. ACM Transactions on Embedded Computing Systems (TECS), 18(5s), 1-27.