Edge AI and Vision
Autonomous driving, industrial defect detection, semantic video retrieval, and practical computer vision pipelines for constrained devices.
Embedded AI and hardware systems
We are a student-led engineering team formed by UESTC and University of Glasgow Electronic Information Engineering graduates, building deployable projects across computer vision, RISC-V platforms, FPGA acceleration, and embedded Linux systems.
Research Direction
Autonomous driving, industrial defect detection, semantic video retrieval, and practical computer vision pipelines for constrained devices.
Cross-compilation toolchains, embedded Linux integration, real-time communication, and low-level performance debugging on heterogeneous platforms.
FPGA and RTL accelerator design for CNNs, keyword spotting, and the group's ongoing AICAS 2026-oriented ViT acceleration work.
People
Research lead
M.S. student in Electrical and Computer Engineering at Vanderbilt University, supported by a tuition scholarship, with a BEng in Electronic Information Engineering from UESTC and the University of Glasgow.
Research member
Master's student at UESTC's School of Information and Communication Engineering, Qingshuihe Campus, with a BEng in Electronic Information Engineering from UESTC and the University of Glasgow.
AI hardware researcher
M.S. student in Electronic Science and Technology at Fudan University, with a BEng in Electronic Information Engineering from UESTC and the University of Glasgow. Focuses on efficient AI hardware.
Research member
Master's student at UESTC's School of Electronic Science and Engineering, Qingshuihe Campus, with a BEng in Electronic Information Engineering from UESTC and the University of Glasgow.
Pet member
Resident morale officer and unofficial review committee, known for strict standards on comfort, curiosity, and nap scheduling.
Current Work
The team is currently exploring FPGA acceleration for Vision Transformer inference. This is active work in progress, not a published result.
Team Work
Deployed and optimized a simulated urban autonomous driving system on a RISC-V platform, with OpenCV cross-compilation, YOLOv5 training on COCO, and INT8 quantization.
Built a distributed multi-MPU embedded system with Ethernet-based communication for real-time data exchange and task coordination.
Designed a surveillance video retrieval pipeline with KCF tracking, FCNN-based semantic event classification, MySQL event tagging, and MQTT communication between Hi3861 and Hi3516DV300.
Developed image-processing algorithms to locate surface defects in thermal insulation materials using edge detection and region segmentation.
Integrated LiDAR obstacle avoidance, VR headset gyroscope data, camera control, and gesture recognition for multimodal navigation.
CNIPA patent application for a communication signal modulation recognition system and method, plus UESTC course-project and graduation awards.
Highlights
Recognition speed improved by 80%, accuracy by 30%, and average CPU usage fell from 96% to 50%.
Tang Ruiqi co-authored a hardware architecture for efficient ViT Token Merging.
Recognition includes a CNIPA patent application and UESTC project and graduation awards.
This site collects the group's members, project work, academic highlights, and technical direction in one public place.
Add paper pages, accelerator demos, benchmark tables, hardware photos, and reproducible notes as the portfolio grows.
Collaboration
Chip Pursuit Lab welcomes collaborations around embedded AI, RISC-V systems, computer vision, FPGA acceleration, and measurable engineering prototypes.