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：近年来，三维数据采集设备的进步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会给资源受限的移动设备带来巨大计算负担，从而限制其潜力的充分发挥。基于设备-边缘协同推理的新兴范式（其中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），我们提出了Branchy-GNN，一种利用边缘计算平台高效执行基于图神经网络（GNN）的点云处理方法。为降低设备端计算成本，Branchy-GNN通过添加分支网络实现早期退出机制；同时，它采用基于学习的联合信源信道编码（JSCC）对中间特征进行压缩，以减少通信开销。实验结果表明，与多种基准方法相比，所提出的Branchy-GNN能显著降低延迟。