Input Feature Pruning for Accelerating GNN Inference on Heterogeneous Platforms-Reference-Cited by-同舟云学术

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Input Feature Pruning for Accelerating GNN Inference on Heterogeneous Platforms

Published:2022-12 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE 29th International Conference on High Performance Computing, Data, and Analytics (HiPC)
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Author:

Yik Jason¹,Kuppannagari Sanmukh R.²,Zeng Hanqing³,Prasanna Viktor K.⁴

Affiliation:

1. Harvard University

2. Case Western Reserve University

3. Meta AI

4. University of Southern California

Funder

National Science Foundation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10106277/10106278/10106342.pdf?arnumber=10106342

Reference26 articles.

1. Decoupling the depth and scope of graph neural networks;zeng,0

2. How powerful are graph neural networks?;xu,0

3. Binary Graph Neural Networks

4. Degree-quant: Quantization-aware training for graph neural networks;tailor,2021

5. Inductive representation learning on large graphs;hamilton;NIPS,2017

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Efficient Inference of Graph Neural Networks Using Local Sensitive Hash;IEEE Transactions on Sustainable Computing;2024-05

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