Xin Zheng
Papers
1
Total Citations
6
H-Index
1
About
Xin Zheng is a researcher at the forefront of efficient deep learning systems, specializing in the intersection of computer vision and hardware acceleration. Their primary contributions lie in designing lightweight neural network architectures and implementing real-time semantic segmentation on FPGA platforms—a critical area for autonomous systems and edge computing. Zheng’s most cited work, “Design and Implementation of Real-time Semantic Segmentation Network Based on FPGA” (2021), addresses the dual challenge of miniaturizing network structures while enabling hardware-efficient deployment. By bridging algorithmic innovation with FPGA-based accelerators, this research demonstrates how to achieve real-time performance without sacrificing accuracy, offering a practical solution for resource-constrained environments. With 6 citations, this paper has provided a foundational reference for engineers and academics exploring embedded AI. Zheng’s work is particularly notable for its emphasis on end-to-end system design, from model compression to hardware mapping, making it highly relevant for students and researchers seeking to understand the practical deployment of deep learning. Their contributions continue to influence the development of low-latency, energy-efficient vision systems.
Research Focus
Key Achievements
Top Papers
- 1