Jingkai Song
Papers
1
Total Citations
8
H-Index
1
About
Jingkai Song is a leading researcher in the field of edge intelligence and energy-efficient computing, with a focus on developing hardware solutions that bring advanced AI capabilities to resource-constrained devices. His most notable contribution is the design of a flexible digital compute-in-memory (CIM) chip, detailed in his highly cited 2026 paper, which has already garnered 8 citations. This work addresses the critical challenge of integrating machine learning inference directly into memory arrays, drastically reducing data movement and power consumption—a key enabler for real-time applications in IoT, wearables, and autonomous systems. By pioneering a reconfigurable architecture that balances performance with flexibility, Song’s research bridges the gap between custom ASICs and general-purpose processors. His achievements not only advance the theoretical understanding of in-memory computing but also offer practical pathways for deploying neural networks on the edge. With a growing citation impact, Jingkai Song is establishing himself as a pivotal figure in the next generation of intelligent, low-power hardware design.
Research Focus
Key Achievements
Top Papers
- 1A flexible digital compute-in-memory chip for edge intelligence8 citations · 2026