ACE-GCN: A Fast Data-driven FPGA Accelerator for GCN Embedding

José Romero Hung, Chao Li*, Pengyu Wang, Chuanming Shao, Jinyang Guo, Jing Wang, Guoyong Shi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

ACE-GCN is a fast and resource/energy-efficient FPGA accelerator for graph convolutional embedding under data-driven and in-place processing conditions. Our accelerator exploits the inherent power law distribution and high sparsity commonly exhibited by real-world graphs datasets. Contrary to other hardware implementations of GCN, on which traditional optimization techniques are employed to bypass the problem of dataset sparsity, our architecture is designed to take advantage of this very same situation. We propose and implement an innovative acceleration approach supported by our "implicit-processing-by-association"concept, in conjunction with a dataset-customized convolutional operator. The computational relief and consequential acceleration effect arise from the possibility of replacing rather complex convolutional operations for a faster embedding result estimation. Based on a computationally inexpensive and super-expedited similarity calculation, our accelerator is able to decide from the automatic embedding estimation or the unavoidable direct convolution operation. Evaluations demonstrate that our approach presents excellent applicability and competitive acceleration value. Depending on the dataset and efficiency level at the target, between 23× and 4,930× PyG baseline, coming close to AWB-GCN by 46% to 81% on smaller datasets and noticeable surpassing AWB-GCN for larger datasets and with controllable accuracy loss levels. We further demonstrate the unique hardware optimization characteristics of our approach and discuss its multi-processing potentiality.

Original languageEnglish
Article number21
JournalACM Transactions on Reconfigurable Technology and Systems
Volume14
Issue number4
DOIs
Publication statusPublished - 14 Sept 2021
Externally publishedYes

Keywords

  • FPGA
  • GCN
  • Graph convolutional neural networks
  • data-driven
  • embedded systems
  • graph embedding
  • graph processing
  • node identification
  • power law distribution
  • sparse datasets

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