BingoCGN, a scalable and efficient graph neural network accelerator that enables inference of real-time, large-scale graphs through graph partitioning, has been developed by researchers at the ...

A research team has introduced a new out-of-core mechanism, Capsule, for large-scale GNN training, which can achieve up to a 12.02× improvement in runtime efficiency, while using only 22.24% of ...

BingoCGN, a scalable and efficient graph neural network accelerator that enables inference of real-time, large-scale graphs ...

Graph computing is a technology that studies the graph in the human world, describing, portraying, analyzing and computing them. Currently, this emerging technology has been widely used, and a ...

“This dovetails with a widely held belief in computer science about graphs as they exist in the real world—the belief that large-scale graphs are always sparse,” he explains. “But I have an ...

In tests using large-scale real-world graph datasets, Capsule outperformed the best existing systems, achieving up to a 12.02x performance improvement while using only 22.24% of the memory.