GROW: A Row-Stationary Sparse-Dense GEMM Accelerator for Memory-Efficient Graph Convolutional Neural Networks

Hwang, Ranggi; Kang, Minhoo; Lee, Jiwon; Kam, Dongyun; Lee, Young‐Joo; Rhu, Minsoo

doi:10.1109/hpca56546.2023.10070983

Cited by 18 publications

(1 citation statement)

References 39 publications

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“…Since the GCNs hardware accelerator HYGCN [1] was proposed in 2020, various GCNs accelerators [1][2][3][4][5][6][7][8][9][10][11] have emerged, one after another, that are different in the calculation method, control flow, and scheduling algorithm, with different advantages in accelerating the GCNs [12,13], such as GCN [14], GIN [15], and GSC [16]. HYGCN [1] proposes a GCNs accelerator composed of an aggregation phase and a combination phase.…”

Section: Introductionmentioning

confidence: 99%

GPGCN: A General-Purpose Graph Convolution Neural Network Accelerator Based on RISC-V ISA Extension

Tang

Zhang

2022

Electronics

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In the past two years, various graph convolution neural networks (GCNs) accelerators have emerged, each with their own characteristics, but their common disadvantage is that the hardware architecture is not programmable and it is optimized for a specific network and dataset. They may not support acceleration for different GCNs and may not achieve optimal hardware resource utilization for datasets of different sizes. Therefore, given the above shortcomings, and according to the development trend of traditional neural network accelerators, this paper proposes and implements GPGCN: a general-purpose GCNs accelerator architecture based on RISC-V instruction set extension, providing the software programming freedom to support acceleration for various GCNs, and achieving the best acceleration efficiency for different GCNs with different datasets. Compared with traditional CPU, and traditional CPU with vector expansion, GPGCN achieves above 1001×, 267× speedup for GCN with the Cora dataset. Compared with dedicated accelerators, GPGCN has software programmability and supports the acceleration of more GCNs.

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