Limei Tang
Papers
1
Total Citations
10
H-Index
1
About
Limei Tang is a leading researcher in energy-efficient hardware design for computer vision and artificial intelligence, with a particular focus on computation-in-memory (CIM) architectures. Her most notable contribution is the development of the 7.7 CV-CIM, a 28nm XOR-derived similarity-aware computation-in-memory system for cost-volume construction—a critical kernel in stereo vision processing used in robotics, autonomous driving, and augmented/virtual reality. This work addresses the significant challenges of large parameter sizes and continuous data accesses in real-time vision applications, achieving high efficiency through innovative similarity-aware design. With over 10 citations for this key paper, Tang’s impact is recognized for advancing practical, low-power solutions that bridge the gap between algorithmic demands and hardware constraints. Her research not only enhances the performance of stereo vision systems but also sets a foundation for future CIM-based accelerators in edge computing. Tang’s achievements underscore her role in shaping next-generation intelligent systems, making her work essential for students and researchers exploring hardware-software co-design for AI.
Research Focus
Key Achievements
Top Papers
- 1