Research

1) Research intelligent system software for next-generation AI and embedded OS such as FreeRTOS, RTEMS, embedded Linux.

2) Advance OS technologies to support heterogeneous processors, CXL memory, and modern sensor systems.

3) Enable efficient and scalable embedded AI systems through future neuromorphic computing and sensor technologies.

We research Linux kernel technologies to optimize resource management across CPUs, GPUs, and NPUs, addressing the challenges of heterogeneous processor scheduling for AI and data-intensive applications. We also study memory management techniques for tiered systems using CXL-attached memory, focusing on efficient page placement, dynamic tiering, and minimizing migration overhead to support large-scale computing workloads.

We propose automatic SNN (Spiking Neural Network) generation methods optimized for neuromorphic hardware, supporting diverse IoT edge service requirements through profiling-based modeling and integration with platforms like Node-RED.

We build intelligent solutions across robotics and healthcare by utilizing advanced sensors such as LiDAR, 3D laser scanners, and AR-based systems, focusing on energy-efficient sensor management and real-time guidance technologies.