This survey overviews recent Graph Convolutional Networks (GCN) advancements,
highlighting their growing significance across various tasks and applications. It underscores …

Large-scale graph processing poses challenges due to its size and irregular memory access
patterns, causing performance degradation in common architectures, such as CPUs and …

Graph analytics are vital in fields such as social networks, biomedical research, and graph
neural networks (GNNs). However, traditional CPUs and GPUs struggle with the memory …

Processing graphs on a large scale presents a range of difficulties, including irregular
memory access patterns, device memory limitations, and the need for effective partitioning in …

Due to the diversity of graph computing applications, the power-law distribution of graph
data, and the high compute-to-memory ratio, traditional architectures face significant …

SOPC (System-on-a-Programmable-Chip) is a System-on-Chip solution based on FPGA
(Field-Programmable Gate Array). SOPC integrates various digital logic modules such as …