A survey of graph convolutional networks (GCNs) in FPGA-based accelerators

Autor:	Marco Procaccini, Amin Sahebi, Roberto Giorgi
Jazyk:	angličtina
Rok vydání:	2024
Předmět:	Graph Convolutional Networks Hardware acceleration FPGA Heterogeneous platform Computer engineering. Computer hardware TK7885-7895 Information technology T58.5-58.64 Electronic computers. Computer science QA75.5-76.95
Zdroj:	Journal of Big Data, Vol 11, Iss 1, Pp 1-36 (2024)
Druh dokumentu:	article
ISSN:	2196-1115
DOI:	10.1186/s40537-024-01022-4
Popis:	Abstract This survey overviews recent Graph Convolutional Networks (GCN) advancements, highlighting their growing significance across various tasks and applications. It underscores the need for efficient hardware architectures to support the widespread adoption and development of GCNs, particularly focusing on platforms like FPGAs known for their performance and energy efficiency. This survey also outlines the challenges in deploying GCNs on hardware accelerators and discusses recent efforts to enhance efficiency. It encompasses a detailed review of the mathematical background of GCNs behind inference and training, a comprehensive review of recent works and architectures, and a discussion on performance considerations and future directions.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/ffad888138d7431c98da43eb466bf6d5 Zobrazit plný text záznamu Full text from SpringerLink View record in DOAJ