  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.
