Xiaoya Fan
Papers
1
Total Citations
3
H-Index
1
About
Xiaoya Fan is a rising researcher in the field of energy-efficient computing, with a focus on hardware acceleration for deep neural networks (DNNs) and edge intelligence. Her work addresses the critical challenge of deploying multiple DNNs simultaneously on resource-constrained edge devices, such as those used in intelligent robotics and autonomous vehicles. Her most cited paper, "Memory-Computing Decoupling: A DNN Multitasking Accelerator With Adaptive Data Arrangement" (2022), introduces a novel architecture that decouples memory and computing to efficiently handle heterogeneous dataflows across different DNN layers. This design enables adaptive data arrangement, significantly improving throughput and energy efficiency in multitasking scenarios. While her citation count is still growing, her contributions are notable for tackling the practical demands of real-world intelligent systems. Fan’s research is particularly relevant for students and engineers working on edge AI, offering a pathway to more flexible and powerful hardware solutions for complex, multi-model workloads.
Research Focus
Key Achievements
Top Papers
- 1