40 citations · 42 across the 8 of their papers we have counts for
8 papers
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…
Performance Modeling Sparse MTTKRP Using Optical Static Random Access Memory on FPGA
Sasindu Wijeratne, Akhilesh Jaiswal, Ajey P. Jacob +2
Electrical static random memory (E-SRAM) is the current standard for internal static memory in Field Programmable Gate Array (FPGA). Despite the dramatic improvement in E-SRAM tech…
Low-latency Mini-batch GNN Inference on CPU-FPGA Heterogeneous Platform
Bingyi Zhang, Hanqing Zeng, Viktor Prasanna
Mini-batch inference of Graph Neural Networks (GNNs) is a key problem in many real-world applications. Recently, a GNN design principle of model depth-receptive field decoupling ha…
Model-Architecture Co-Design for High Performance Temporal GNN Inference on FPGA
Hongkuan Zhou, Bingyi Zhang, Rajgopal Kannan +2
Temporal Graph Neural Networks (TGNNs) are powerful models to capture temporal, structural, and contextual information on temporal graphs. The generated temporal node embeddings ou…
HP-GNN: Generating High Throughput GNN Training Implementation on CPU-FPGA Heterogeneous Platform
Yi-Chien Lin, Bingyi Zhang, Viktor Prasanna
Graph Neural Networks (GNNs) have shown great success in many applications such as recommendation systems, molecular property prediction, traffic prediction, etc. Recently, CPU-FPG…