Renhao Fan
Papers
1
Total Citations
9
H-Index
1
About
Renhao Fan is a rising researcher in computer architecture, whose work focuses on enabling efficient, parallel execution of deep neural networks (DNNs) on resource-constrained platforms. His key research areas include many-core architectures, in-memory computing, and hardware acceleration for artificial intelligence. Fan’s most notable contribution is the development of MAICC, a lightweight many-core architecture that integrates in-cache computing to support multi-DNN parallel inference. This design addresses the growing demand for flexibility and performance in autonomous driving and intelligent robotics, where diverse neural networks must run simultaneously. By moving computation into the cache hierarchy, MAICC reduces data movement overhead and energy consumption, offering a scalable solution for real-time AI workloads. With his 2023 paper already garnering 9 citations, Fan is establishing himself as an innovator in bridging the gap between hardware efficiency and algorithmic complexity. His work is particularly relevant for students and researchers exploring edge AI, embedded systems, and domain-specific architectures, demonstrating how thoughtful hardware-software co-design can unlock new capabilities in next-generation intelligent systems.
Research Focus
Key Achievements
Top Papers
- 1