Characterization and Analysis of Deep Learning for 3D Point Cloud Analytics

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A point cloud is a collection of points, which is measured by time-of-flight information from LiDAR sensors, forming geometrical representations of the surrounding environment. With the algorithmic success of deep learning networks, point clouds are not only used in traditional application domains like localization or HD map construction but also in a variety of avenues including object classification, 3D object detection, or semantic segmentation. While point cloud analytics are gaining significant traction in both academia and industry, the computer architecture community has only recently begun exploring this important problem space. In this paper, we conduct a detailed, end-to-end characterization on deep learning based point cloud analytics workload, root-causing the frontend data preparation stage as a significant performance limiter. Through our findings, we discuss possible future directions to motivate continued research in this emerging application domain.
Publisher
IEEE COMPUTER SOC
Issue Date
2021-07
Language
English
Article Type
Article
Citation

IEEE COMPUTER ARCHITECTURE LETTERS, v.20, no.2, pp.106 - 109

ISSN
1556-6056
DOI
10.1109/LCA.2021.3099117
URI
http://hdl.handle.net/10203/287511
Appears in Collection
EE-Journal Papers(저널논문)
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