The use of domain-specific accelerators is becoming prominent for a variety of emerging
domains such as graph analytics and HPC, where most of the computations revolve around …

FPGA-based graph processing accelerators, enabling extensive customization, have
demonstrated significant energy efficiency over general computing engines like CPUs and …

With the development of loT and edge computing, data analytics on edge has become
popular, and text analytics directly on compression (TADOC) has been proven to be a …

Graph mining is of critical use in a number of fields such as social networks, knowledge
graphs, and fraud detection. As an NP-complete problem, accelerating computation …

The introduction of High Bandwidth Memory (HBM) to the FPGA chip makes it possible for
an FPGA-based accelerator to leverage the huge memory bandwidth of HBM to improve its …

Field-programmable gate array (FPGA) is an ideal candidate for accelerating graph neural
networks (GNNs). However, FPGA reconfiguration is a time-consuming process when …

With the merits of high productivity and ease of use, highlevel synthesis (HLS) tools bring
hope to fast FPGA-based architecture development. However, their usability and popularity …

This work presents a novel reconfigurable architecture for Low Latency Graph Neural
Network (LL-GNN) designs for particle detectors, delivering unprecedented low latency …

Temporal graph processing is used to handle the snapshots of the temporal graph, which
concerns changes in graph over time. Although several software/hardware solutions have …

Graph processing is pivotal in deriving insights from complex data structures but faces
performance limitations due to the irregular nature of graphs. Traditional general-purpose …