Jeongmin Shin
Papers
1
Total Citations
8
H-Index
1
About
Jeongmin Shin is a leading researcher in energy-efficient artificial intelligence and robotics hardware, specializing in real-time autonomous systems. His work centers on developing fully integrated system-on-chip (SoC) architectures that enable simultaneous localization and mapping (SLAM) for mobile robots and autonomous vehicles. Shin’s most notable contribution is the "20.6 LSPU: A Fully Integrated Real-Time LiDAR-SLAM SoC with Point-Neural-Network Segmentation and Multi-Level kNN Acceleration" (2024), which addresses critical limitations of RGB-based visual SLAM processors—namely restricted field-of-view and inaccurate depth perception—by leveraging LiDAR data. This design integrates point-neural-network segmentation and multi-level k-nearest neighbor acceleration, achieving robust performance for autonomous driving tasks. With 8 citations already, this work demonstrates his impact in pushing the boundaries of edge-computing hardware for robotics. Shin’s research bridges the gap between algorithmic advances and practical silicon implementation, offering scalable solutions for real-time perception and navigation. His achievements highlight a commitment to creating efficient, deployable systems that empower next-generation autonomous technologies, making him a key figure in the intersection of hardware design and artificial intelligence.
Research Focus
Key Achievements
Top Papers
- 1