GP3D: 3D NAND Based In-Memory Graph Processing Accelerator

In this work, we proposed GP3D: 3D NAND based processing-in-memory (PIM) accelerator for highly parallel and energy efficient graph processing. Although graph processing algorithms give informative and useful features from the real world graphs, innate randomness of the graph data pattern incurs inefficient processing in conventional computing architectures. Some previous works on nonvolatile memory (NVM) based PIM architecture have been proposed with vector-matrix multiplication (VMM) in memory array, but the limited memory capacity of the architecture causes frequent write operations on memory cells. Our GP3D architecture is based on the standard 3D NAND array structure without any structural changes which can utilize the ultra-high density of 3D NAND to store the large scale graphs in a single die. VMM operations on 3D NAND array enables efficient processing of PageRank algorithm, one of the representative algorithms of graph processing. To process the graphs with compressed format without conversion to the dense format, we exploited ternary content addressable memory (TCAM) function on 3D NAND. GP3D can eliminate the data loading burdens from storage device to memory which gives significant enhancement of the latency and energy efficiency. Evaluated by custom designed graph processing simulator, GP3D showed 30–61 times boosted speed and 18–64 times better energy efficiency over the GPU based system for various graph datasets.