Graph is a well known data structure to represent the associated relationships in a variety of
applications, eg, data science and machine learning. Despite a wealth of existing efforts on …

Simple graph algorithms such as PageRank have been the target of numerous hardware
accelerators. Yet, there also exist much more complex graph mining algorithms for problems …

Constructing energy-efficient database systems to reduce economic costs and
environmental impact has been studied for 10 years. With the emergence of the big data …

Graph Convolutional Networks (GCNs) have emerged as the state-of-the-art deep learning
model for representation learning on graphs. It is challenging to accelerate training of GCNs …

FPGA has been an emerging computing infrastructure in datacenters benefiting from
features of fine-grained parallelism, energy efficiency, and reconfigurability. Meanwhile …

The performance of large-scale graph processing suffers from challenges including poor
locality, lack of scalability, random access pattern, and heavy data conflicts. Some …

A critical step of genome sequence analysis is the mapping of sequenced DNA fragments
(ie, reads) collected from an individual to a known linear reference genome sequence (ie …

Graph processing workloads are memory intensive with irregular access patterns and large
memory footprint resulting in low data locality. Their popular software implementations …

Sparse-Matrix Dense-Matrix multiplication (SpMM) is the key operator for a wide range of
applications including scientific computing, graph processing, and deep learning …

The wide deployment of Deep Neural Networks (DNN) in high-performance cloud
computing platforms brought to light multi-tenant cloud field-programmable gate arrays …