mlsanityData to 7 Oct 2026

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

arXiv 2203.00158 ↗First posted 1 Mar 2022cs.AR cs.AI cs.LGcited by 4

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Abstract (arXiv)

Graph convolutional neural networks (GCNs) have emerged as a key technology in various application domains where the input data is relational. A unique property of GCNs is that its two primary execution stages, aggregation and combination, exhibit drastically different dataflows. Consequently, prior GCN accelerators tackle this research space by casting the aggregation and combination stages as a series of sparse-dense matrix multiplication. However, prior work frequently suffers from inefficient data movements, leaving significant performance left on the table. We present GROW, a GCN accelerator based on Gustavson's algorithm to architect a row-wise product based sparse-dense GEMM accelerator. GROW co-designs the software/hardware that strikes a balance in locality and parallelism for GCNs, achieving significant energy-efficiency improvements vs. state-of-the-art GCN accelerators.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 32 of its 65 references that are in the map. First posted on arXiv 1 Mar 2022.

Builds on

32 of 65 references.

References · 32

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20 more
  1. 2017SCNN: An Accelerator for Compressed-sparse Convolutional Neural Networkscited by 132
  2. 2019PyTorch-BigGraph: A Large-scale Graph Embedding Systemcited by 118
  3. 2020A3: Accelerating Attention Mechanisms in Neural Networks with Approximationcited by 97
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  5. 2018NVIDIA Tensor Core Programmability, Performance & Precisioncited by 77
  6. 2020HyGCN: A GCN Accelerator with Hybrid Architecturecited by 73
  7. 2020GNNAdvisor: An Adaptive and Efficient Runtime System for GNN Acceleration on GPUscited by 51
  8. 2019PCONV: The Missing but Desirable Sparsity in DNN Weight Pruning for Real-time Execution on Mobile Devicescited by 48
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  14. 2017Compressing DMA Engine: Leveraging Activation Sparsity for Training Deep Neural Networkscited by 36
  15. 2019GraphACT: Accelerating GCN Training on CPU-FPGA Heterogeneous Platformscited by 29
  16. 2020Centaur: A Chiplet-based, Hybrid Sparse-Dense Accelerator for Personalized Recommendationscited by 21
  17. 2020Tensor Casting: Co-Designing Algorithm-Architecture for Personalized Recommendation Trainingcited by 13
  18. 2019Beyond the Memory Wall: A Case for Memory-centric HPC System for Deep Learningcited by 9
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  20. 2019NeuMMU: Architectural Support for Efficient Address Translations in Neural Processing Unitscited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 1; 2024: 1; 2025: 0; 2026: 1.

4 papers.

  1. 2023MaxK-GNN: Extremely Fast GPU Kernel Design for Accelerating Graph Neural Networks Trainingcited by 25
  2. 2022LL-GNN: Low Latency Graph Neural Networks on FPGAs for High Energy Physicscited by 12
  3. 2024GCN-ABFT: Low-Cost Online Error Checking for Graph Convolutional Networkscited by 2
  4. 2026Reducing the GPU Memory Bottleneck with Lossless Compression for ML -- Extendedcited by 0