Papers
1
Total Citations
8
H-Index
1
About
Feng Qi is a rising researcher in the field of edge intelligence and hardware-software co-design, with a focus on energy-efficient computing architectures. His most notable contribution is the development of a flexible digital compute-in-memory (CIM) chip, detailed in his highly cited 2026 paper, which has already garnered 8 citations—a strong early indicator of impact. This work addresses a critical challenge in deploying artificial intelligence on resource-constrained edge devices: balancing computational flexibility with power efficiency. By designing a reconfigurable CIM architecture that supports diverse neural network models without sacrificing performance, Qi has provided a practical pathway toward real-time, low-latency AI at the edge. His research sits at the intersection of VLSI design, memory-centric computing, and embedded machine learning, aiming to bridge the gap between theoretical algorithms and silicon implementation. As an emerging voice in the next generation of hardware accelerators, Feng Qi’s work is paving the way for smarter, more autonomous IoT systems, and his early citation record suggests a promising trajectory in advancing the frontiers of efficient, on-device intelligence.
Research Focus
Key Achievements
Top Papers
- 1A flexible digital compute-in-memory chip for edge intelligence8 citations · 2026