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DTSTART;VALUE=DATE:20201015
DTEND;VALUE=DATE:20201231
DTSTAMP:20260422T115208
CREATED:20201015T142444Z
LAST-MODIFIED:20201015T142444Z
UID:22804-1602720000-1609372799@coe.northeastern.edu
SUMMARY:Meet Your Graduate Student Ambassadors!
DESCRIPTION:Meet your Student Ambassadors! Prospective and Admitted Graduate Students are invited to meet their Student Ambassador via Unibuddy.
URL:https://coe.northeastern.edu/event/meet-your-graduate-student-ambassadors/
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DTSTART;TZID=America/New_York:20201204T150000
DTEND;TZID=America/New_York:20201204T160000
DTSTAMP:20260422T115208
CREATED:20201123T155204Z
LAST-MODIFIED:20201123T155204Z
UID:23279-1607094000-1607097600@coe.northeastern.edu
SUMMARY:ECE MS Thesis Defense: Yuxuan Cai
DESCRIPTION:MS Thesis Defense: Real-Time Object Detection on Mobile Devices via Compression-Compilation Co-Design \nYuxuan Cai \nLocation: Zoom Link \nAbstract: The rapid development and wide utilization of object detection techniques have aroused attention on both accuracy and speed of object detectors. However\, the current state-of-the- art object detection works are either accuracy-oriented using a large model but leading to high latency or speed-oriented using a lightweight model but sacrificing accuracy. In this work\, we propose YOLObile framework\, a real-time object detection on mobile devices via compression compilation co-design. A novel block-punched pruning scheme is proposed for any kernel size. To improve computational efficiency on mobile devices\, a GPU-CPU collaborative scheme is adopted along with advanced compiler-assisted optimizations. Experimental results indicate that our pruning scheme achieves 14× compression rate of YOLOv4 with 49.0 mAP. Under our YOLObile framework\, we achieve 17 FPS inference speed using GPU on Samsung Galaxy S20. By incorporating our proposed GPU-CPU collaborative scheme\, the inference speed is increased to 19.1 FPS\, and outperforms the original YOLOv4 by 5× speedup.
URL:https://coe.northeastern.edu/event/ece-ms-thesis-defense-yuxuan-cai/
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