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 Institute of Science Tokyo, Japan. This breakthrough framework utilizes an innovative cross-partition message quantization technique and a novel training algorithm to significantly reduce memory demands and increase computational and energy efficiency.
发布者：Dr.Durant，转转请注明出处：https://robotalks.cn/new-framework-reduces-memory-usage-and-boosts-energy-efficiency-for-large-scale-ai-graph-analysis/
