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SPA-GCN: Efficient and Flexible GCN Accelerator with an Application for Graph Similarity Computation

arXiv 2111.05936 ↗First posted 10 Nov 2021cs.LG cs.AR cs.DCcited by 0

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Abstract (arXiv)

While there have been many studies on hardware acceleration for deep learning on images, there has been a rather limited focus on accelerating deep learning applications involving graphs. The unique characteristics of graphs, such as the irregular memory access and dynamic parallelism, impose several challenges when the algorithm is mapped to a CPU or GPU. To address these challenges while exploiting all the available sparsity, we propose a flexible architecture called SPA-GCN for accelerating Graph Convolutional Networks (GCN), the core computation unit in deep learning algorithms on graphs. The architecture is specialized for dealing with many small graphs since the graph size has a significant impact on design considerations. In this context, we use SimGNN, a neural-network-based graph matching algorithm, as a case study to demonstrate the effectiveness of our architecture. The experimental results demonstrate that SPA-GCN can deliver a high speedup compared to a multi-core CPU implementation and a GPU implementation, showing the efficiency of our design.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 22 of its 80 references that are in the map. First posted on arXiv 10 Nov 2021.

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22 of 80 references.

References · 22

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,414
  3. 2018Graph Convolutional Neural Networks for Web-Scale Recommender Systemscited by 1,133
  4. 2017MoleculeNet: A Benchmark for Molecular Machine Learningcited by 1,037
  5. 2015Convolutional Networks on Graphs for Learning Molecular Fingerprintscited by 751
  6. 2018T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Predictioncited by 399
  7. 2016EIE: Efficient Inference Engine on Compressed Deep Neural Networkcited by 291
  8. 2019Graph Matching Networks for Learning the Similarity of Graph Structured Objectscited by 202
  9. 2017SCNN: An Accelerator for Compressed-sparse Convolutional Neural Networkscited by 132
  10. 2017Bit Fusion: Bit-Level Dynamically Composable Architecture for Accelerating Deep Neural Networkscited by 95
  11. 2018SimGNN: A Neural Network Approach to Fast Graph Similarity Computationcited by 89
  12. 2020HyGCN: A GCN Accelerator with Hybrid Architecturecited by 73
10 more
  1. 2017CirCNN: Accelerating and Compressing Deep Neural Networks Using Block-CirculantWeight Matricescited by 53
  2. 2018Learning-based Efficient Graph Similarity Computation via Multi-Scale Convolutional Set Matchingcited by 45
  3. 2019EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networkscited by 42
  4. 2019Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Modelscited by 36
  5. 2019GraphACT: Accelerating GCN Training on CPU-FPGA Heterogeneous Platformscited by 29
  6. 2021Dual-side Sparse Tensor Corecited by 27
  7. 2020Multi-View Graph Neural Networks for Molecular Property Predictioncited by 19
  8. 2018Packing Sparse Convolutional Neural Networks for Efficient Systolic Array Implementations: Column Combining Under Joint Optimizationcited by 15
  9. 2015Gunrock: A High-Performance Graph Processing Library on the GPUcited by 14
  10. 2015NXgraph: An Efficient Graph Processing System on a Single Machinecited by 2

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