Branchy-GNN: A Device-Edge Co-Inference Framework for Efficient Point Cloud Processing

Jiawei Shao, Yuyi Mao, Jun Zhang, Haowei Zhang

    Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review


    The recent advancements of three-dimensional (3D) data acquisition devices have spurred a new breed of applications that rely on point cloud data processing. However, processing a large volume of point cloud data brings a significant workload on resource-constrained mobile devices, prohibiting from unleashing their full potentials. Built upon the emerging paradigm of device-edge co-inference, where an edge device extracts and transmits the intermediate feature to an edge server for further processing, we propose Branchy-GNN for efficient graph neural network (GNN) based point cloud processing by leveraging edge computing platforms. In order to reduce the on-device computational cost, the Branchy-GNN adds branch networks for early exiting. Besides, it employs learning-based joint source-channel coding (JSCC) for the intermediate feature compression to reduce the communication overhead. Our experimental results demonstrate that the proposed Branchy-GNN secures a significant latency reduction compared with several benchmark methods.
    Original languageEnglish
    Title of host publication2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2021)
    Place of PublicationToronto, Ontario, Canada
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Publication statusPublished - Jun 2021


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