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 emerged as powerful tools for data analysis,
surpassing traditional algorithms in various applications. However, the growing size of real …

Training recommendation systems (RecSys) faces several challenges as it requires the
“data preprocessing” stage to preprocess an ample amount of raw data and feed them to the …

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 …

Metagenomics, the study of the genome sequences of diverse organisms in a common
environment, has led to significant advances in many fields. Since the species present in a …

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

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 …