Heterogeneous graph neural networks (HGNNs) based on metapath exhibit powerful
capturing of rich structural and semantic information in the heterogeneous graph. HGNNs …

Characterizing graph neural networks (GNNs) is essential for identifying performance
bottlenecks and facilitating their deployment. Despite substantial work in this area, a …

The memory-intensive embedding layers of the personalized recommendation systems are
the performance bottleneck as they demand large memory bandwidth and exhibit irregular …

Metapath-based heterogeneous graph models (MHGM) show excellent performance in
learning semantic and structural information in heterogeneous graphs. Metapath matching is …

Characterizing and understanding graph neural networks (GNNs) is essential for identifying
performance bottlenecks and facilitating their deployment in parallel and distributed …

The Oriented FAST and Rotated BRIEF (ORB) algorithm plays a crucial role in rapidly
extracting image keypoints. However, in the domain of high-frame-rate real-time …

Heterogeneous graph neural networks (HGNNs) have recently demonstrated significant
advantages of capturing powerful structural and semantic information in heterogeneous …

Graph neural network (GNN) accelerators have achieved prominent performance speedup
on static graphs but fallen with inefficiency on dynamic graphs. The reason is that in dynamic …

Dynamic graph convolutional networks (DGCNs) have been increasingly used to extend
machine learning techniques to applications that involve graph-structured data with …

Dynamic graph neural networks (DGNNs) play a crucial role in applications that require
inferencing on graph-structured data, where the connectivity and features of the graph …