A Task-Adaptive In-Situ ReRAM Computing for Graph Convolutional Networks-Reference-Cited by-同舟云学术

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A Task-Adaptive In-Situ ReRAM Computing for Graph Convolutional Networks

Published:2024-09 Issue:9 Volume:43 Page:2635-2646
ISSN:0278-0070
Container-title:IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
language:
Short-container-title:IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

Author:

He Yintao¹^ORCID,Li Bing²,Wang Ying³^ORCID,Liu Cheng¹^ORCID,Li Huawei¹^ORCID,Li Xiaowei¹^ORCID

Affiliation:

1. State Key Laboratory of Processors, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

2. Academy for Multidisciplinary Studies, Capital Normal University, Beijing, China

3. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, CICS, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/43/10643378/10463614.pdf?arnumber=10463614

Reference34 articles.

1. Semi-supervised classification with graph convolutional networks;Kipf;arXiv:1609.02907,2017

2. Inductive representation learning on large graphs;Hamilton

3. A Comprehensive Survey on Graph Neural Networks

4. Graph information bottleneck for subgraph recognition;Yu

5. Improving the accuracy, scalability, and performance of graph neural networks with Roc;Jia

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