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2020 | OriginalPaper | Buchkapitel

GNN-PIM: A Processing-in-Memory Architecture for Graph Neural Networks

verfasst von : Zhao Wang, Yijin Guan, Guangyu Sun, Dimin Niu, Yuhao Wang, Hongzhong Zheng, Yinhe Han

Erschienen in: Advanced Computer Architecture

Verlag: Springer Singapore

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Abstract

Graph neural networks (GNNs) have attracted increasing interests in recent years. Due to the poor data locality and huge data movement during GNN inference, it is challenging to employ GNN to process large-scale graphs. Fortunately, processing-in-memory (PIM) architecture has been widely investigated as a promising approach to address the “Memory Wall”. In this work, we propose a PIM architecture to accelerate GNN inference. We develop an optimized dataflow to leverage the inherent parallelism of GNNs. Targeting the dataflow, we further propose a hierarchical NoC to perform concurrent data transmission. Experimental results show that our design can outperform prior works significantly.

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Titel: GNN-PIM: A Processing-in-Memory Architecture for Graph Neural Networks
verfasst von: Zhao Wang
Yijin Guan
Guangyu Sun
Dimin Niu
Yuhao Wang
Hongzhong Zheng
Yinhe Han
Verlag: Springer Singapore
Buch: Advanced Computer Architecture
Print ISBN: 978-981-15-8134-2

Electronic ISBN: 978-981-15-8135-9

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-981-15-8135-9_6

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