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

Abstract

AbstractThis 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.