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HyGCN: A GCN Accelerator with Hybrid Architecture

arXiv 2001.02514 ↗First posted 7 Jan 2020cs.DCcited by 73

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Abstract (arXiv)

In this work, we first characterize the hybrid execution patterns of GCNs on Intel Xeon CPU. Guided by the characterization, we design a GCN accelerator, HyGCN, using a hybrid architecture to efficiently perform GCNs. Specifically, first, we build a new programming model to exploit the fine-grained parallelism for our hardware design. Second, we propose a hardware design with two efficient processing engines to alleviate the irregularity of Aggregation phase and leverage the regularity of Combination phase. Besides, these engines can exploit various parallelism and reuse highly reusable data efficiently. Third, we optimize the overall system via inter-engine pipeline for inter-phase fusion and priority-based off-chip memory access coordination to improve off-chip bandwidth utilization. Compared to the state-of-the-art software framework running on Intel Xeon CPU and NVIDIA V100 GPU, our work achieves on average 1509× speedup with 2500× energy reduction and average 6.5× speedup with 10× energy reduction, respectively.

Key facts. Cited by 73 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Jan 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 17; 2022: 24; 2023: 17; 2024: 9; 2025: 5; 2026: 1.

73 papers.

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  1. 2021A Unified Lottery Ticket Hypothesis for Graph Neural Networkscited by 118
  2. 2022Trustworthy Graph Neural Networks: Aspects, Methods and Trendscited by 88
  3. 2021Accelerating Large Scale Real-Time GNN Inference using Channel Pruningcited by 41
  4. 2021Sampling methods for efficient training of graph convolutional networks: A surveycited by 40
  5. 2021FLAT: An Optimized Dataflow for Mitigating Attention Bottleneckscited by 38
  6. 2022Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysiscited by 29
  7. 2021Understanding GNN Computational Graph: A Coordinated Computation, IO, and Memory Perspectivecited by 28
  8. 2021Early-Bird GCNs: Graph-Network Co-Optimization Towards More Efficient GCN Training and Inference via Drawing Early-Bird Lottery Ticketscited by 27
  9. 2022Survey on Graph Neural Network Acceleration: An Algorithmic Perspectivecited by 26
  10. 2023MaxK-GNN: Extremely Fast GPU Kernel Design for Accelerating Graph Neural Networks Trainingcited by 25
  11. 2022I-GCN: A Graph Convolutional Network Accelerator with Runtime Locality Enhancement through Islandizationcited by 21
  12. 2021Do We Need Anisotropic Graph Neural Networks?cited by 18
38 more
  1. 2023A Survey on Graph Neural Network Acceleration: Algorithms, Systems, and Customized Hardwarecited by 18
  2. 2021Graph Neural Networks for Charged Particle Tracking on FPGAscited by 17
  3. 2022MGG: Accelerating Graph Neural Networks with Fine-grained intra-kernel Communication-Computation Pipelining on Multi-GPU Platformscited by 16
  4. 2021G-CoS: GNN-Accelerator Co-Search Towards Both Better Accuracy and Efficiencycited by 15
  5. 2022A Comprehensive Survey on Distributed Training of Graph Neural Networkscited by 15
  6. 2021GCoD: Graph Convolutional Network Acceleration via Dedicated Algorithm and Accelerator Co-Designcited by 12
  7. 2022Model-Architecture Co-Design for High Performance Temporal GNN Inference on FPGAcited by 12
  8. 2022Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challengescited by 12
  9. 2022SmartSAGE: Training Large-scale Graph Neural Networks using In-Storage Processing Architecturescited by 12
  10. 2022LL-GNN: Low Latency Graph Neural Networks on FPGAs for High Energy Physicscited by 12
  11. 2021BlockGNN: Towards Efficient GNN Acceleration Using Block-Circulant Weight Matricescited by 11
  12. 2022Neural Graph Databasescited by 11
  13. 2022GNN at the Edge: Cost-Efficient Graph Neural Network Processing over Distributed Edge Serverscited by 11
  14. 2022FlowGNN: A Dataflow Architecture for Real-Time Workload-Agnostic Graph Neural Network Inferencecited by 10
  15. 2023Accurate, Low-latency, Efficient SAR Automatic Target Recognition on FPGAcited by 10
  16. 2023HOT: Higher-Order Dynamic Graph Representation Learning with Efficient Transformerscited by 10
  17. 2022GenGNN: A Generic FPGA Framework for Graph Neural Network Accelerationcited by 9
  18. 2023MaGNAS: A Mapping-Aware Graph Neural Architecture Search Framework for Heterogeneous MPSoC Deploymentcited by 9
  19. 2022Characterizing and Understanding Distributed GNN Training on GPUscited by 7
  20. 2023GHOST: A Graph Neural Network Accelerator using Silicon Photonicscited by 7
  21. 2021GNNSampler: Bridging the Gap between Sampling Algorithms of GNN and Hardwarecited by 6
  22. 2021GNNear: Accelerating Full-Batch Training of Graph Neural Networks with Near-Memory Processingcited by 6
  23. 2022SUGAR: Efficient Subgraph-level Training via Resource-aware Graph Partitioningcited by 6
  24. 2023Semi-decentralized Inference in Heterogeneous Graph Neural Networks for Traffic Demand Forecasting: An Edge-Computing Approachcited by 5
  25. 2022GROW: A Row-Stationary Sparse-Dense GEMM Accelerator for Memory-Efficient Graph Convolutional Neural Networkscited by 4
  26. 2023SGCN: Exploiting Compressed-Sparse Features in Deep Graph Convolutional Network Acceleratorscited by 4
  27. 2021VersaGNN: a Versatile accelerator for Graph neural networkscited by 3
  28. 2021GNNIE: GNN Inference Engine with Load-balancing and Graph-Specific Cachingcited by 3
  29. 2024HDReason: Algorithm-Hardware Codesign for Hyperdimensional Knowledge Graph Reasoningcited by 3
  30. 2021LW-GCN: A Lightweight FPGA-based Graph Convolutional Network Acceleratorcited by 2
  31. 2022Bottleneck Analysis of Dynamic Graph Neural Network Inference on CPU and GPUcited by 2
  32. 2023Exploiting On-chip Heterogeneity of Versal Architecture for GNN Inference Accelerationcited by 2
  33. 2023InkStream: Real-time GNN Inference on Streaming Graphs via Incremental Updatecited by 2
  34. 2023Architectural Implications of GNN Aggregation Programming Abstractionscited by 2
  35. 2023GraNNDis: Efficient Unified Distributed Training Framework for Deep GNNs on Large Clusterscited by 2
  36. 2023Multicoated and Folded Graph Neural Networks with Strong Lottery Ticketscited by 2
  37. 2024GCV-Turbo: End-to-end Acceleration of GNN-based Computer Vision Tasks on FPGAcited by 2
  38. 2024Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligencecited by 2

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