Xiaoqiang Xiang

University of Electronic Science and Technology of China

Papers

Total Citations

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About

Xiaoqiang Xiang is a leading researcher in energy-efficient reconfigurable AI hardware, with a primary focus on embedded object detection and tracking processors for smart robotics and drone applications. His major contributions center on developing specialized neural network processors that achieve high energy efficiency while supporting flexible, online object learning—a critical capability for autonomous systems that must adapt to new targets in real time. His most cited works, including "An Energy-Efficient Reconfigurable AI-Based Object Detection and Tracking Processor Supporting Online Object Learning" (2022, 6 citations) and "RAODAT: An Energy-Efficient Reconfigurable AI-based Object Detection and Tracking Processor with Online Learning" (2021, 5 citations), introduce novel processing engines that go beyond standard NN accelerators by incorporating dedicated hardware for bounding box regression and tracking tasks. These innovations address the growing demand for intelligent, low-power embedded processors in autonomous platforms. Xiang’s work is notable for bridging the gap between algorithmic flexibility and hardware efficiency, enabling smart robots to perform dynamic object detection and tracking without sacrificing energy performance. His research continues to shape the future of edge AI in robotics and autonomous systems.