# I-GCN: A Graph Convolutional Network Accelerator with Runtime Locality Enhancement through Islandization

arXiv: https://arxiv.org/abs/2203.03606 · HTML: https://mlsanity.com/paper/2203.03606/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 55 references that are in the map.
- First posted on arXiv 7 Mar 2022.

## Builds on (most cited first, 21 of 21)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [Spectral Networks and Locally Connected Networks on Graphs](https://mlsanity.com/paper/1312.6203/) (2013)
- [FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling](https://mlsanity.com/paper/1801.10247/) (2018)
- [Graph Transformer Networks](https://mlsanity.com/paper/1911.06455/) (2019)
- [EIE: Efficient Inference Engine on Compressed Deep Neural Network](https://mlsanity.com/paper/1602.01528/) (2016)
- [Modeling polypharmacy side effects with graph convolutional networks](https://mlsanity.com/paper/1802.00543/) (2018)
- [AliGraph: A Comprehensive Graph Neural Network Platform](https://mlsanity.com/paper/1902.08730/) (2019)
- [Computing Graph Neural Networks: A Survey from Algorithms to Accelerators](https://mlsanity.com/paper/2010.00130/) (2020)
- [HyGCN: A GCN Accelerator with Hybrid Architecture](https://mlsanity.com/paper/2001.02514/) (2020)
- [EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks](https://mlsanity.com/paper/1909.00155/) (2019)
- [GRIP: A Graph Neural Network Accelerator Architecture](https://mlsanity.com/paper/2007.13828/) (2020)
- [GraphACT: Accelerating GCN Training on CPU-FPGA Heterogeneous Platforms](https://mlsanity.com/paper/2001.02498/) (2019)
- [G-CoS: GNN-Accelerator Co-Search Towards Both Better Accuracy and Efficiency](https://mlsanity.com/paper/2109.08983/) (2021)
- [Guiding Cascading Failure Search with Interpretable Graph Convolutional Network](https://mlsanity.com/paper/2001.11553/) (2020)
- [Rubik: A Hierarchical Architecture for Efficient Graph Learning](https://mlsanity.com/paper/2009.12495/) (2020)
- [A Closer Look at Lightweight Graph Reordering](https://mlsanity.com/paper/2001.08448/) (2020)

## Built on by (most cited first, 21 of 21)

- [Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis](https://mlsanity.com/paper/2205.09702/) (2022)
- [MaxK-GNN: Extremely Fast GPU Kernel Design for Accelerating Graph Neural Networks Training](https://mlsanity.com/paper/2312.08656/) (2023)
- [Accel-GCN: High-Performance GPU Accelerator Design for Graph Convolution Networks](https://mlsanity.com/paper/2308.11825/) (2023)
- [A Survey on Graph Neural Network Acceleration: Algorithms, Systems, and Customized Hardware](https://mlsanity.com/paper/2306.14052/) (2023)
- [LL-GNN: Low Latency Graph Neural Networks on FPGAs for High Energy Physics](https://mlsanity.com/paper/2209.14065/) (2022)
- [FlowGNN: A Dataflow Architecture for Real-Time Workload-Agnostic Graph Neural Network Inference](https://mlsanity.com/paper/2204.13103/) (2022)
- [SGCN: Exploiting Compressed-Sparse Features in Deep Graph Convolutional Network Accelerators](https://mlsanity.com/paper/2301.10388/) (2023)
- [Bottleneck Analysis of Dynamic Graph Neural Network Inference on CPU and GPU](https://mlsanity.com/paper/2210.03900/) (2022)
- [Exploiting On-chip Heterogeneity of Versal Architecture for GNN Inference Acceleration](https://mlsanity.com/paper/2308.02749/) (2023)
- [Architectural Implications of GNN Aggregation Programming Abstractions](https://mlsanity.com/paper/2310.12184/) (2023)
- [GCV-Turbo: End-to-end Acceleration of GNN-based Computer Vision Tasks on FPGA](https://mlsanity.com/paper/2404.07188/) (2024)
- [Efficient GNN Training Through Structure-Aware Randomized Mini-Batching](https://mlsanity.com/paper/2504.18082/) (2025)
- [H-GCN: A Graph Convolutional Network Accelerator on Versal ACAP Architecture](https://mlsanity.com/paper/2206.13734/) (2022)
- [Slice-and-Forge: Making Better Use of Caches for Graph Convolutional Network Accelerators](https://mlsanity.com/paper/2301.09813/) (2023)
- [GNNBuilder: An Automated Framework for Generic Graph Neural Network Accelerator Generation, Simulation, and Optimization](https://mlsanity.com/paper/2303.16459/) (2023)
- [Accelerating Generic Graph Neural Networks via Architecture, Compiler, Partition Method Co-Design](https://mlsanity.com/paper/2308.08174/) (2023)
- [NeuraChip: Accelerating GNN Computations with a Hash-based Decoupled Spatial Accelerator](https://mlsanity.com/paper/2404.15510/) (2024)
- [SiHGNN: Leveraging Properties of Semantic Graphs for Efficient HGNN Acceleration](https://mlsanity.com/paper/2408.15089/) (2024)
- [Efficient Message Passing Architecture for GCN Training on HBM-based FPGAs with Orthogonal Topology On-Chip Networks](https://mlsanity.com/paper/2411.03857/) (2024)
- [MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators](https://mlsanity.com/paper/2501.01951/) (2025)
- [HePGA: A Heterogeneous Processing-in-Memory based GNN Training Accelerator](https://mlsanity.com/paper/2508.16011/) (2025)

## Abstract (arXiv)

Graph Convolutional Networks (GCNs) have drawn tremendous attention in the past three years. Compared with other deep learning modalities, high-performance hardware acceleration of GCNs is as critical but even more challenging. The hurdles arise from the poor data locality and redundant computation due to the large size, high sparsity, and irregular non-zero distribution of real-world graphs. In this paper we propose a novel hardware accelerator for GCN inference, called I-GCN, that significantly improves data locality and reduces unnecessary computation. The mechanism is a new online graph restructuring algorithm we refer to as islandization. The proposed algorithm finds clusters of nodes with strong internal but weak external connections. The islandization process yields two major benefits. First, by processing islands rather than individual nodes, there is better on-chip data reuse and fewer off-chip memory accesses. Second, there is less redundant computation as aggregation for common/shared neighbors in an island can be reused. The parallel search, identification, and leverage of graph islands are all handled purely in hardware at runtime working in an incremental pipeline. This is done without any preprocessing of the graph data or adjustment of the GCN model structure. Experimental results show that I-GCN can significantly reduce off-chip accesses and prune 38% of aggregation operations, leading to performance speedups over CPUs, GPUs, the prior art GCN accelerators of 5549x, 403x, and 5.7x on average, respectively.