  Graph Neural Networks (GNNs) have gained significant momentum recently due to
their capability to learn on unstructured graph data. Dynamic GNNs (DGNNs) are
the current state-of-the-art for point cloud applications; such applications
(viz. autonomous driving) require real-time processing at the edge with tight
latency and memory constraints. Conducting performance analysis on such DGNNs,
thus, becomes a crucial task to evaluate network suitability.
  This paper presents a profiling analysis of EdgeConv-based DGNNs applied to
point cloud inputs. We assess their inference performance in terms of
end-to-end latency and memory consumption on state-of-the-art CPU and GPU
platforms. The EdgeConv layer has two stages: (1) dynamic graph generation
using k-Nearest Neighbors (kNN) and, (2) node feature updation. The addition of
dynamic graph generation via kNN in each (EdgeConv) layer enhances network
performance compared to networks that work with the same static graph in each
layer; such performance enhancement comes, however, at the added computational
cost associated with the dynamic graph generation stage (via kNN algorithm).
Understanding its costs is essential for identifying the performance bottleneck
and exploring potential avenues for hardware acceleration. To this end, this
paper aims to shed light on the performance characteristics of EdgeConv-based
DGNNs for point cloud inputs. Our performance analysis on a state-of-the-art
EdgeConv network for classification shows that the dynamic graph construction
via kNN takes up upwards of 95% of network latency on the GPU and almost 90% on
the CPU. Moreover, we propose a quasi-Dynamic Graph Neural Network (qDGNN) that
halts dynamic graph updates after a specific depth within the network to
significantly reduce the latency on both CPU and GPU whilst matching the
original networks inference accuracy.
