Papers
2
Total Citations
11
H-Index
2
About
Jiayan Gan is a leading researcher in energy-efficient artificial intelligence, with a primary focus on reconfigurable hardware architectures for embedded computer vision. Their work addresses the critical challenge of deploying intelligent object detection and tracking on power-constrained platforms like smart drones and robots. Gan’s major contributions center on developing specialized neural network processors that achieve high energy efficiency while supporting flexible, online object learning—a capability essential for real-time adaptation in dynamic environments. Their most-cited paper, “An Energy-Efficient Reconfigurable AI-Based Object Detection and Tracking Processor Supporting Online Object Learning” (2022, 6 citations), along with the closely related “RAODAT” work (2021, 5 citations), introduces novel processing engines that go beyond standard neural network accelerators to handle bounding box regression and tracking-specific tasks. By integrating reconfigurable logic with online learning, Gan’s designs enable autonomous systems to recognize and track new objects without retraining, marking a significant step toward truly intelligent edge devices. Their research is highly relevant for students and engineers working at the intersection of AI, low-power VLSI design, and robotics.
Research Focus
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Top Papers
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