Graph neural networks (GNNs) are a type of deep learning models that are trained on
graphs and have been successfully applied in various domains. Despite the effectiveness of …

Abstract Processing-in-memory (PIM) techniques have gained much attention from computer
architecture researchers, and significant research effort has been invested in exploring and …

Graph neural networks (GNNs) are among the most powerful tools in deep learning. They
routinely solve complex problems on unstructured networks, such as node classification …

Graph neural network (GNN) is a promising emerging application for link prediction,
recommendation, etc. Existing hardware innovation is limited to single-machine GNN (SM …

Abstract Graph Neural Networks (GNNs) are of great value in numerous applications and
promote the development of cognitive intelligence, due to the capability of modeling non …

Graph Convolutional Network (GCN) models have attracted attention given their high
accuracy in interpreting graph data. One of the primary building blocks of a GCN model is …

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 …

Coarse-Grained Reconfigurable Architectures (CGRAs) are promising solutions to achieve
more performance with the end of Moore's law. Thanks to word-level programmability, they …

Graph Neural Networks (GNNs) enable effective machine learning on graph-structured data,
but their performance and scalability are often limited by the irregular structure and large …

Graph convolutional network (GCN) has emerged as a powerful model for many graph-
related tasks. In conventional von Neumann architectures, massive data movement and …