Yikai Cui
Papers
1
Total Citations
9
H-Index
1
About
Yikai Cui is a rising researcher in computer architecture, with a focus on efficient hardware-software co-design for emerging AI workloads. His work centers on developing lightweight many-core architectures that enable parallel inference of multiple deep neural networks (DNNs)—a critical capability for resource-constrained edge systems like autonomous vehicles and intelligent robots. In his highly cited 2023 paper, "MAICC: A Lightweight Many-core Architecture with In-Cache Computing for Multi-DNN Parallel Inference," Cui introduces an innovative in-cache computing paradigm that significantly reduces data movement overhead while maintaining the flexibility to handle diverse network topologies. This work has already garnered 9 citations, reflecting its timely relevance to the growing demand for real-time, multi-model AI processing. By tackling the fundamental tension between programmability and efficiency, Cui’s contributions are paving the way for next-generation embedded AI platforms. His research is particularly notable for bridging the gap between traditional many-core design and the unique demands of modern deep learning inference, making him a promising voice in the field of energy-efficient, domain-specific architectures.
Research Focus
Key Achievements
Top Papers
- 1