Byungin Moon
Papers
2
Total Citations
8
H-Index
2
About
Byungin Moon is a leading researcher in the design of energy-efficient hardware architectures for real-time embedded vision systems, with a primary focus on stereo matching processors. His work directly addresses the critical challenges of minimizing hardware resources and power consumption for applications like intelligent robots and autonomous vehicles. Moon’s major contributions include the development of a modified adaptive support weight algorithm and a disparity search range estimation scheme, which significantly enhance the efficiency of stereo matching processors. His 2017 paper on this topic has garnered 6 citations, reflecting its impact on the field. Additionally, Moon has explored the potential of 3D stacking technology using through-silicon vias (TSVs) to further reduce power and area in these processors, as detailed in his 2017 study on technology scaling. This forward-looking work, with 2 citations, underscores his role in advancing hardware design for next-generation embedded systems. Moon’s research is pivotal for enabling high-performance, low-power vision processing in autonomous platforms.
Research Focus
Key Achievements
Top Papers
- 1
- 2