Yabing Wang
Papers
1
Total Citations
10
H-Index
1
About
Yabing Wang is a leading researcher in energy-efficient computing architectures, with a primary focus on computation-in-memory (CIM) systems for advanced vision applications. Their most notable contribution is the development of the 7.7 CV-CIM, a 28nm XOR-derived similarity-aware computation-in-memory chip specifically designed for cost-volume construction—a critical kernel in stereo vision processing. This work addresses the fundamental challenge of accurately computing pixel similarities between paired images, which is essential for robotic navigation, autonomous driving, and augmented/virtual reality systems. By tackling the dual bottlenecks of large parameter sizes and consecutive data accesses inherent in real-time stereo vision, Wang's design achieves significant improvements in energy efficiency and processing speed. The CV-CIM architecture demonstrates how similarity-aware computing can dramatically reduce the computational overhead of matching pixel correspondences, making it highly impactful for edge-AI applications. With this work accumulating 10 citations since 2023, Wang's research is gaining recognition for bridging the gap between hardware efficiency and the demanding computational needs of modern computer vision, positioning them as an emerging innovator in the field of in-memory computing for real-time perception systems.
Research Focus
Key Achievements
Top Papers
- 1