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.

翻译：三维（3D）数据采集设备的最新进展催生了依赖点云数据处理的新型应用。然而，处理大规模点云数据给资源受限的移动设备带来了巨大计算负担，阻碍其充分发挥潜力。基于设备-边缘协同推理这一新兴范式（即边缘设备提取中间特征并传输至边缘服务器进行后续处理），我们提出Branchy-GNN框架，通过利用边缘计算平台实现基于图神经网络（GNN）的高效点云处理。为降低设备端计算成本，Branchy-GNN引入分支网络实现早期退出机制。此外，该框架采用基于学习的联合源信道编码（JSCC）对中间特征进行压缩，以减少通信开销。实验结果表明，与若干基准方法相比，所提出的Branchy-GNN能够显著降低延迟。