Deep learning has gained superior accuracy on Euclidean structure data in neural networks.
As a result, non-Euclidean structure data, such as graph data, has more sophisticated …

Graph analytics is the basis for many modern applications, eg, machine learning and
streaming data problems. With an unprecedented increase in data size of many emerging …

Advances in graph neural network (GNN)-based algorithms enable machine learning on
relational data. GNNs are computationally demanding since they rely upon backpropagation …

As its core computation, a self-attention mechanism gauges pairwise correlations across the
entire input sequence. Despite favorable performance, calculating pairwise correlations 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 …

Recently, in light of the success of quantum computers, research teams have actively
developed quantum-inspired computers using classical computing technology. One notable …

Graph convolutional networks (GCNs) have become a powerful deep learning approach for
graph-structured data. Different from traditional neural networks such as convolutional …

Graph convolutional network (GCN) is a promising but computing-and memory-intensive
learning model. Processing-in-memory (PIM) architecture based on the resistive random …

Sparse matrix multiplication is widely used in various practical applications. Different
accelerators have been proposed to speed up sparse matrix-dense vector multiplication …

Computing-in-memory (CIM) is emerging as a promising architecture to accelerate graph
convolutional networks (GCNs) normally bounded by redundant and irregular memory …