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 …

Bulk bitwise operations, ie, bitwise operations on large bit vectors, are prevalent in a wide
range of important application domains, including databases, graph processing, genome …

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

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 …

Training deep neural network (DNN) models is a resource-intensive, iterative process. For
this reason, nowadays, complex optimizers like Adam are widely adopted as it increases the …

Graph neural networks (GNNs) have shown to significantly improve graph analytics. Existing
systems for GNN training are primarily designed for homogeneous graphs. In industry …

Computational storage adds computing to storage devices, providing potential benefits in
offload, data-reduction, and lower energy. Successful computational SSD architectures …

Storage, as a core function and fundamental component of computers, provides services for
saving and reading digital data. The increasing complexity of data operations and storage …

Near-data processing (NDP) architecture is promised to break the bottleneck of data
movement in many scenarios (eg, databases and recommendation systems), which limits …

Training Graph Neural Networks on large graphs is challenging due to the need to store
graph data and move them along the memory hierarchy. In this work, we tackle this by …