# SiHGNN: Leveraging Properties of Semantic Graphs for Efficient HGNN Acceleration

arXiv: https://arxiv.org/abs/2408.15089 · HTML: https://mlsanity.com/paper/2408.15089/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 34 of its 58 references that are in the map.
- First posted on arXiv 27 Aug 2024.

## Builds on (most cited first, 30 of 34)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Open Graph Benchmark: Datasets for Machine Learning on Graphs](https://mlsanity.com/paper/2005.00687/) (2020)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Heterogeneous Graph Attention Network](https://mlsanity.com/paper/1903.07293/) (2019)
- [Composition-based Multi-Relational Graph Convolutional Networks](https://mlsanity.com/paper/1911.03082/) (2019)
- [MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding](https://mlsanity.com/paper/2002.01680/) (2020)
- [Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks](https://mlsanity.com/paper/1909.01315/) (2019)
- [Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networks](https://mlsanity.com/paper/2112.14936/) (2021)
- [A Survey of Heterogeneous Information Network Analysis](https://mlsanity.com/paper/1511.04854/) (2015)
- [Relational Graph Attention Network for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2004.12362/) (2020)
- [GCN-RL Circuit Designer: Transferable Transistor Sizing with Graph Neural Networks and Reinforcement Learning](https://mlsanity.com/paper/2005.00406/) (2020)
- [HyGCN: A GCN Accelerator with Hybrid Architecture](https://mlsanity.com/paper/2001.02514/) (2020)
- [ScisummNet: A Large Annotated Corpus and Content-Impact Models for Scientific Paper Summarization with Citation Networks](https://mlsanity.com/paper/1909.01716/) (2019)
- [Simple and Efficient Heterogeneous Graph Neural Network](https://mlsanity.com/paper/2207.02547/) (2022)
- [Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning](https://mlsanity.com/paper/1903.08948/) (2019)
- [DeepGate: Learning Neural Representations of Logic Gates](https://mlsanity.com/paper/2111.14616/) (2021)
- [DeepGate2: Functionality-Aware Circuit Representation Learning](https://mlsanity.com/paper/2305.16373/) (2023)
- [GraphACT: Accelerating GCN Training on CPU-FPGA Heterogeneous Platforms](https://mlsanity.com/paper/2001.02498/) (2019)
- [Characterizing and Understanding GCNs on GPU](https://mlsanity.com/paper/2001.10160/) (2020)
- [I-GCN: A Graph Convolutional Network Accelerator with Runtime Locality Enhancement through Islandization](https://mlsanity.com/paper/2203.03606/) (2022)
- [Task-Guided and Path-Augmented Heterogeneous Network Embedding for Author Identification](https://mlsanity.com/paper/1612.02814/) (2016)
- [A Comprehensive Survey on Distributed Training of Graph Neural Networks](https://mlsanity.com/paper/2211.05368/) (2022)
- [Item Tagging for Information Retrieval: A Tripartite Graph Neural Network based Approach](https://mlsanity.com/paper/2008.11567/) (2020)
- [Architectural Implications of Graph Neural Networks](https://mlsanity.com/paper/2009.00804/) (2020)
- [FlowGNN: A Dataflow Architecture for Real-Time Workload-Agnostic Graph Neural Network Inference](https://mlsanity.com/paper/2204.13103/) (2022)
- [Characterizing and Understanding Distributed GNN Training on GPUs](https://mlsanity.com/paper/2204.08150/) (2022)
- [GDR-HGNN: A Heterogeneous Graph Neural Networks Accelerator Frontend with Graph Decoupling and Recoupling](https://mlsanity.com/paper/2404.04792/) (2024)
- [Multi-node Acceleration for Large-scale GCNs](https://mlsanity.com/paper/2207.07258/) (2022)

## Abstract (arXiv)

Heterogeneous Graph Neural Networks (HGNNs) have expanded graph representation learning to heterogeneous graph fields. Recent studies have demonstrated their superior performance across various applications, including medical analysis and recommendation systems, often surpassing existing methods. However, GPUs often experience inefficiencies when executing HGNNs due to their unique and complex execution patterns. Compared to traditional Graph Neural Networks, these patterns further exacerbate irregularities in memory access. To tackle these challenges, recent studies have focused on developing domain-specific accelerators for HGNNs. Nonetheless, most of these efforts have concentrated on optimizing the datapath or scheduling data accesses, while largely overlooking the potential benefits that could be gained from leveraging the inherent properties of the semantic graph, such as its topology, layout, and generation. In this work, we focus on leveraging the properties of semantic graphs to enhance HGNN performance. First, we analyze the Semantic Graph Build (SGB) stage and identify significant opportunities for data reuse during semantic graph generation. Next, we uncover the phenomenon of buffer thrashing during the Graph Feature Processing (GFP) stage, revealing potential optimization opportunities in semantic graph layout. Furthermore, we propose a lightweight hardware accelerator frontend for HGNNs, called SiHGNN. This accelerator frontend incorporates a tree-based Semantic Graph Builder for efficient semantic graph generation and features a novel Graph Restructurer for optimizing semantic graph layouts. Experimental results show that SiHGNN enables the state-of-the-art HGNN accelerator to achieve an average performance improvement of 2.95×.