Mengqi Yu
Papers
2
Total Citations
23
H-Index
2
About
Mengqi Yu is a leading researcher in energy-efficient embedded AI, specializing in FPGA-based accelerators for computer vision and robotics. Their work focuses on bridging the gap between complex deep neural networks and resource-constrained hardware, enabling real-time performance on mobile and autonomous platforms. Yu’s most-cited paper, “EDSSA: An Encoder-Decoder Semantic Segmentation Networks Accelerator on OpenCL-Based FPGA Platform” (2020, 12 citations), pioneers a hardware-software co-design approach that dramatically reduces the computational burden of semantic segmentation, a critical task for autonomous driving and intelligent surveillance. Building on this, their 2021 study “An FPGA Based Energy Efficient DS-SLAM Accelerator for Mobile Robots in Dynamic Environment” (11 citations) introduces a power-efficient accelerator for visual semantic SLAM, allowing robots to simultaneously map and localize in changing environments while fusing semantic understanding. By achieving high throughput with minimal energy consumption, Yu’s contributions directly enable practical deployment of advanced AI in drones, service robots, and edge devices. Their work stands as a cornerstone for the next generation of autonomous systems that must operate under strict power and latency constraints.
Research Focus
Key Achievements
Top Papers
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