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ReGNN: A Redundancy-Eliminated Graph Neural Networks Accelerator

  • Published:2022-04 Issue: Volume: Page:
  • ISSN:
  • Container-title:2022 IEEE International Symposium on High-Performance Computer Architecture (HPCA)
  • language:
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Author:

Chen Cen1,Li Kenli1,Li Yangfan1,Zou Xiaofeng1

Affiliation:

1. Hunan University,College of Information Science and Engineering,China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

IEEE

Reference37 articles.

1. GraphABCD: Scaling Out Graph Analytics with Asynchronous Block Coordinate Descent

2. Alleviating Irregularity in Graph Analytics Acceleration

3. HyGCN: A GCN Accelerator with Hybrid Architecture

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

5. Deep learning on graphs: A survey;zhang;IEEE Transactions on Knowledge and Data Engineering,2020

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4. TT-GNN: Efficient On-Chip Graph Neural Network Training via Embedding Reformation and Hardware Optimization;56th Annual IEEE/ACM International Symposium on Microarchitecture;2023-10-28

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