2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDASPS-2.5
Paper Title TAMING VOTING ALGORITHMS ON GPUS FOR AN EFFICIENT CONNECTED COMPONENT ANALYSIS ALGORITHM
Authors Florian Lemaitre, Arthur Hennequin, Lionel Lacassagne, Sorbonne Université, France
SessionASPS-2: Algorithm/Architecture Co-design
LocationGather.Town
Session Time:Tuesday, 08 June, 16:30 - 17:15
Presentation Time:Tuesday, 08 June, 16:30 - 17:15
Presentation Poster
Topic Applied Signal Processing Systems: Design & Synthesis [DIS-ARCH, DIS-LPWR]
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Connected Component Analysis is vastly used as a building block for many Computer Vision algorithms from many fields like medical image processing, surveillance, or autonomous driving. It extends Connected Component Labeling by computing some features of the connected components like their bounding box or their surface. As such, Connected Component Analysis is a voting algorithm just like histogram computation or Hough transform. Voting algorithms are difficult on many-core architectures like GPUs because of the serialization of atomic memory accesses. The trend to increase the number of cores makes this issue even more critical. This paper explores multiple ways to reduce those conflicts for voting algorithms and especially for Connected Component Analysis. We show that our new algorithm is from 4 up to 10 times faster than State-of-the-Art on average on an Nvidia A100.