Jianxun Yang
Papers
1
Total Citations
3
H-Index
1
About
Jianxun Yang is a rising researcher in the field of energy-efficient hardware design for computer vision, with a focus on computation-in-memory (CIM) architectures and stereo vision processing. His key research areas include hybrid-domain computing, similarity-aware memory design, and cost-volume construction for 3D perception systems. Yang’s most notable contribution is the development of CV-CIM, a novel hybrid-domain XOR-derived similarity-aware computation-in-memory framework that significantly reduces the parameter sizes and energy demands of cost-volume construction—a critical bottleneck in stereo vision for robotics, autonomous vehicles, and augmented reality. This work, published in 2024, has already garnered 3 citations, signaling early impact in a rapidly growing field. By addressing the continuous challenge of balancing computational efficiency with accuracy in real-time 3D reconstruction, Yang’s research bridges the gap between hardware limitations and the demanding requirements of autonomous systems. His innovative approach to integrating similarity-aware logic directly into memory arrays positions him as a promising contributor to next-generation edge AI accelerators, where low-power, high-speed vision processing is essential.
Research Focus
Key Achievements
Top Papers
- 1