Graph neural networks (GNNs) can extract features by learning both the representation of
each objects (ie, graph nodes) and the relationship across different objects (ie, the edges …

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

Recently, Graph Neural Networks (GNNs) have been receiving a spotlight as a powerful tool
that can effectively serve various inference tasks on graph structured data. As the size of real …

Prior in-storage computing (ISC) solutions show fundamental drawbacks when applied to
GNN acceleration. First, they obey a strict ordering of GNN neighbor sampling. Such …

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

The data collected by the distributed high-speed network has multiple sources. Therefore, in
order to realize the rapid integration of multi-source data, this paper designs a rapid data …

In order to provide users with more humanized and intelligent big data knowledge services,
a research method of intelligent big information retrieval of Smart Library Based on graph …

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), an emerging algorithm for graph computing, has
achieved promising performance in graphstructure tasks. To achieve acceleration for data …

With the continuous development of intelligent computing power, neural networks are widely
used in all walks of life. Traditional neural networks, such as convolutional neural networks …