Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent
years owing to their capability to model and learn from graph-structured data. Such an ability …

With graphs rapidly growing in size and deeper graph neural networks (GNNs) emerging,
the training and inference of GNNs become increasingly expensive. Existing network weight …

Graph neural networks (GNNs) are a class of deep learning algorithms that learn from
graphs, networks and relational data. They have found applications throughout the sciences …

Graph neural networks (GNNs) have emerged due to their success at modeling graph data.
Yet, it is challenging for GNNs to efficiently scale to large graphs. Thus, distributed GNNs …

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 …

Memory latency and bandwidth are significant bottlenecks in designing in-memory indexes.
Processing-in-memory (PIM), an emerging hardware design approach, alleviates this …

Recently, Graph Neural Networks (GNNs) have become state-of-the-art algorithms for
analyzing non-euclidean graph data. However, to realize efficient GNN training is …

Aggregating features from neighbor vertices is a fundamental operation in graph convolution
network (GCN). However, the sparsity in graph data creates poor spatial and temporal …

Graph convolutional networks (GCNs) have shown remarkable learning capabilities when
processing graph-structured data found inherently in many application areas. GCNs …

Resistive random-access memory (ReRAM)-based manycore architectures enable
acceleration of graph neural network (GNN) inference and training. GNNs exhibit …