40 citations · 42 across the 8 of their papers we have counts for
4 papers · 1 filter
Exploiting On-chip Heterogeneity of Versal Architecture for GNN Inference Acceleration
Paul Chen, Pavan Manjunath, Sasindu Wijeratne +2
Graph Neural Networks (GNNs) have revolutionized many Machine Learning (ML) applications, such as social network analysis, bioinformatics, etc. GNN inference can be accelerated by…
Dynasparse: Accelerating GNN Inference through Dynamic Sparsity Exploitation
Bingyi Zhang, Viktor Prasanna
Graph Neural Network (GNN) inference is used in many real-world applications. Data sparsity in GNN inference, including sparsity in the input graph and the GNN model, offer opportu…
HitGNN: High-throughput GNN Training Framework on CPU+Multi-FPGA Heterogeneous Platform
Yi-Chien Lin, Bingyi Zhang, Viktor Prasanna
As the size of real-world graphs increases, training Graph Neural Networks (GNNs) has become time-consuming and requires acceleration. While previous works have demonstrated the po…
GraphAGILE: An FPGA-based Overlay Accelerator for Low-latency GNN Inference
Bingyi Zhang, Hanqing Zeng, Viktor Prasanna
This paper presents GraphAGILE, a domain-specific FPGA-based overlay accelerator for graph neural network (GNN) inference. GraphAGILE consists of (1) \emph{a novel unified architec…