Knowledge graphs (KGs) have achieved significant attention in recent years, particularly in
the area of the Semantic Web as well as gaining popularity in other application domains …

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 …

The data access bottleneck in memory-intensive applications has motivated various
architectural innovations in the main memory system, with Near-Data Processing (NDP) and …

Today's computing systems require moving data back-and-forth between computing
resources (eg, CPUs, GPUs, accelerators) and off-chip main memory so that computation …

Graph Convolutional Networks (GCNs) have successfully incorporated deep learning to
graph structures for social network analysis, bio-informatics, etc. The execution pattern of …

Graph Neural Networks (GNNs) have evolved as a powerful tool for understanding and
processing graphical data. However, their effectiveness is often hindered by the …