A Survey on Graph Neural Network Acceleration:A Hardware Perspective

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外文摘要：Graph neural networks(GNNs)have emerged as powerful approaches to learn knowledge about graphs and vertices.The rapid employment of GNNs poses requirements for processing efficiency.Due to incompati-bility of general platforms,dedicated hardware devices and platforms are developed to efficiently accelerate training and inference of GNNs.We conduct a survey on hardware acceleration for GNNs.We first include and introduce re-cent advances of the domain,and then provide a methodology of categorization to classify existing works into three categories.Next,we discuss optimization techniques adopted at different levels.And finally we propose suggestions on future directions to facilitate further works.

外文关键词：

Graph neural networksDeep learning accelerationDomain-specific architectureHardware accel-erator

作者：

Shi CHEN、Jingyu LIU、Li SHEN

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作者单位：

School of Computer,National University of Defense Technology,Changsha 410073,China

Key Laboratory of Advanced Microprocessor Chips and Systems,Changsha 410073,China

基金：

National Natural Science Foundation of China Key ProgramNational Natural Science Foundation of China General Program

项目编号：

6203200161972407

出版年：

2024

DOI：

10.23919/cje.2023.00.135

电子学报(英文)

CSTPCDEI

ISSN：1022-4653

年,卷(期)：2024.33(3)