Papers
11
Total Citations
1,493
H-Index
7
About
Jincheng Yu is a researcher working at the intersection of embedded systems, computer vision, and autonomous robotics. His most influential contribution, "Going Deeper with Embedded FPGA Platform for Convolutional Neural Network" (2016, over 1,260 citations), established him as a pioneer in deploying deep learning models on resource-constrained hardware, addressing the critical challenge of running computationally intensive CNNs efficiently on embedded FPGA platforms. This foundational work has had a lasting impact on edge AI and embedded computing communities worldwide. Beyond hardware acceleration, Yu has made significant contributions to multi-robot systems and autonomous exploration, developing collaborative SLAM frameworks, submap-based exploration strategies, and Gaussian Mixture Model mapping methods that enable robots to navigate unknown environments without external positioning. His 2013 work on deep learning-based robotic grasping (76 citations) further demonstrates an early commitment to practical robot perception. More recently, Yu has expanded into self-supervised depth-pose learning for dynamic scenes, standardized benchmarking for autonomous exploration through Explore-Bench, and drone-based radio mapping for logistics applications. Together, his body of work reflects a consistent vision: making sophisticated AI and robotic perception viable on real-world, resource-limited platforms — a challenge increasingly central to modern robotics research.
Research Focus
Key Achievements
Top Papers
- 1Going Deeper with Embedded FPGA Platform for Convolutional Neural Network1,260 citations · 2016
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- 5CNN-based Monocular Decentralized SLAM on embedded FPGA9 citations · 2020
- 6INCA: INterruptible CNN Accelerator for Multi-tasking in Embedded Robots9 citations · 2020
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