Dynasparse: Accelerating GNN Inference through Dynamic Sparsity Exploitation-Reference-Cited by-同舟云学术

Dynasparse: Accelerating GNN Inference through Dynamic Sparsity Exploitation

Published:2023-05 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS)
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Author:

Zhang Bingyi¹,Prasanna Viktor¹

Affiliation:

1. University of Southern California,Los Angeles,California,USA

Funder

National Science Foundation

Xilinx

Publisher

IEEE

Link

Reference24 articles.

1. Simplifying graph convolutional networks;wu;International Conference on Machine Learning,2019

2. Revisiting semi-supervised learning with graph embeddings;yang;International Conference on Machine Learning,2016

3. How powerful are graph neural networks?;xu,2018

4. Graph-SAINT: Graph sampling based inductive learning method;zeng;International Conference on Learning Representations,0

5. Triple sparsification of graph convolutional networks without sacrificing the accuracy;rahman,0

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3. Accelerating GNN-Based SAR Automatic Target Recognition on HBM-Enabled FPGA;2023 IEEE High Performance Extreme Computing Conference (HPEC);2023-09-25

4. Exploiting On-Chip Heterogeneity of Versal Architecture for GNN Inference Acceleration;2023 33rd International Conference on Field-Programmable Logic and Applications (FPL);2023-09-04