Xueming Li
Papers
1
Total Citations
7
H-Index
1
About
Dr. Xueming Li is a leading researcher in reconfigurable computing and hardware acceleration for artificial intelligence, with a particular focus on field-programmable gate array (FPGA)-based architectures. Their most cited work, "Efficient field‐programmable gate array‐based reconfigurable accelerator for deep convolution neural network" (2021, 7 citations), addresses the critical challenge of deploying deep convolutional neural networks (DCNNs) on resource-constrained mobile and embedded platforms. Dr. Li's major contribution lies in designing high-efficiency, reconfigurable accelerators that dramatically reduce the billions of multiply-accumulate operations required for DCNN inference, enabling real-time AI processing without sacrificing performance. This work is notable for bridging the gap between the computational demands of modern AI and the practical limitations of edge devices, offering a scalable solution for applications ranging from autonomous systems to smart sensors. By pioneering FPGA-based reconfigurable architectures, Dr. Li has advanced the field of efficient deep learning hardware, making AI more accessible and deployable in real-world, low-power environments.
Research Focus
Key Achievements
Top Papers
- 1