A KAIST research team has developed a new technology that enables to process a large-scale graph algorithm without storing the graph in the main memory or on disks. Named as T-GPS (Trillion-scale ...
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New framework reduces memory usage and boosts energy efficiency for large-scale AI graph analysis
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 ...
BingoCGN employs cross-partition message quantization to summarize inter-partition message flow, which eliminates the need for irregular off-chip memory access and utilizes a fine-grained structured ...
Triangle counting stands as a fundamental task in graph theory and network science, offering critical insights into the structural properties of complex systems. By enumerating all sets of three ...
A research team has developed a new technology that enables to process a large-scale graph algorithm without storing the graph in the main memory or on disks. A KAIST research team has developed a new ...
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