GNNHLS: Evaluating Graph Neural Network Inference via High-Level Synthesis-Reference-Cited by-同舟云学术

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GNNHLS: Evaluating Graph Neural Network Inference via High-Level Synthesis

Published:2023-11-06 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 41st International Conference on Computer Design (ICCD)
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Author:

Zhao Chenfeng¹,Dong Zehao¹,Chen Yixin¹,Zhang Xuan¹,Chamberlain Roger D.¹

Affiliation:

1. Washington Univ. in St. Louis,McKelvey School of Engineering

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10360938/10360940/10360963.pdf?arnumber=10360963

Reference17 articles.

1. GenGNN: A generic FPGA framework for graph neural network acceleration;Abi-Karam,2022

2. Residual gated graph convnets;Bresson,2017

3. Transformations of High-Level Synthesis Codes for High-Performance Computing

4. CktGNN: Circuit graph neural network for electronic design automation;Dong,2023

5. Benchmarking graph neural networks;Dwivedi;Journal of Machine Learning Research,2020

Cited by 2 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. HLS Taking Flight: Toward Using High-Level Synthesis Techniques in a Space-Borne Instrument;Proceedings of the 21st ACM International Conference on Computing Frontiers;2024-05-07

2. HLPerf: Demystifying the Performance of HLS-based Graph Neural Networks with Dataflow Architectures;ACM Transactions on Reconfigurable Technology and Systems;2024-04-02

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