Work

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

Year: 2024

Type: article

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.

Source: Journal Of Big Data

DOI: 10.1186/s40537-024-01022-4

Authors: Marco Procaccini, Amin Sahebi, Roberto Giorgi

Institutions: University of Siena, Consorzio Interuniversitario Nazionale per l'Informatica, National Research Council, University of Florence

Language: English

Topic: Embedded Systems Design Techniques

Subfield: Hardware and Architecture

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,090