Our research has focused on system optimizations for interesting applications in modern eras, which include deep learning frameworks and big data analysis platforms. Our current interests include:
- Compiler Optimization for GPU/FPGA Computing
- Platform Optimization for Big Data/Machine Learning Frameworks
- System Software Design for Non-Volatile Memories
- Static/Dynamic Program Analysis for Performance and Security
- Loop analysis based optimizations for deep learning compilers, Samsung Advanced Institute of Technology (SAIT), 2023.03-2024.02
- Investigate loop analysis for general fusion/fission methods integrated with schedulers for multiple devices.
- Analysis of applications for zns-ssd storage systems and application, Samsung Electronics, 2022.06-2024.05
- Investigate application characteristics for specialized-SDDs and explore design space.
- Improving FPGA Programmability for Big Data Analytics, National Research Foundation (NRF), 2021.03-2025.02
- Investigate techniques for accelerator programmability on big data analytics frameworks.
- Research on Edge-Native Operating Systems for Edge Micro-Data-Centers, Institute of Information & Communications Technology Planning & Evaluation (IITP), 2021.04-2024.12
- Develop abstraction techniques in compilers for HW-accelerators in EMDC operating systems.
[ Past Grants ]