GRIP: A Graph Neural Network Accelerator Architecture

Kiningham, Kevin; Ré, Christopher; Levis, Philip

doi:10.48550/arxiv.2007.13828

Cited by 15 publications

(18 citation statements)

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“…Most of these works accelerate the inference process or a certain phase in the training of GCN leveraging hardware characteristics and specially designed tools [74][75][76][77][78][79][80].…”

Section: Challenges and Future Directionsmentioning

confidence: 99%

Sampling Methods for Efficient Training of Graph Convolutional Networks: A Survey

Liu

Yan

Deng

et al. 2022

IEEE/CAA J. Autom. Sinica

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Graph Convolutional Networks (GCNs) have received significant attention from various research fields due to the excellent performance in learning graph representations. Although GCN performs well compared with other methods, it still faces challenges. Training a GCN model for large-scale graphs in a conventional way requires high computation and memory costs. Therefore, motivated by an urgent need in terms of efficiency and scalability in training GCN, sampling methods are proposed and achieve a significant effect. In this paper, we categorize sampling methods based on the sampling mechanisms and provide a comprehensive survey of sampling methods for efficient training of GCN. To highlight the characteristics and differences of sampling methods, we present a detailed comparison within each category and further give an overall comparative analysis for the sampling methods in all categories. Finally, we discuss some challenges and future research directions of the sampling methods.

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Section: Challenges and Future Directionsmentioning

confidence: 99%

Sampling Methods for Efficient Training of Graph Convolutional Networks: A Survey

Liu

Yan

Deng

et al. 2022

IEEE/CAA J. Autom. Sinica

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“…EnGN [16] GCN [9], GraphSage-Max [14], GatedGCN [11], GRN [16], R-GCN [15] HyGCN [17] GCN, GraphSage-Mean [14], GIN [24], DiffPool [12] Auten et al [19] GCN, GAT [13], PGNN [19] AWB-GCN [18] GCN GRIP [20] GCN, GraphSage-Max, GIN, GatedGCN…”

Section: Accelerators Algorithms Supportedmentioning

confidence: 99%

Characterizing the Communication Requirements of GNN Accelerators: A Model-Based Approach

Guirado

Jain

Abadal

et al. 2021

2021 IEEE International Symposium on Circuits and Systems (ISCAS)

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Relational data present in real world graph representations demands for tools capable to study it accurately. In this regard Graph Neural Network (GNN) is a powerful tool, wherein various models for it have also been developed over the past decade. Recently, there has been a significant push towards creating accelerators that speed up the inference and training process of GNNs. These accelerators, however, do not delve into the impact of their dataflows on the overall data movement and, hence, on the communication requirements. In this paper, we formulate analytical models that capture the amount of data movement in the most recent GNN accelerator frameworks. Specifically, the proposed models capture the dataflows and hardware setup of these accelerator designs and expose their scalability characteristics for a set of hardware, GNN model and input graph parameters. Additionally, the proposed approach provides means for the comparative analysis of the vastly different GNN accelerators.

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“…Hardware acceleration of GNN inference, and graph processing in general, has been an active area of study (Besta et al, 2019;Gui et al, 2019;Ming Xiong, 2020). Nurvitadhi et al (2014); Ozdal et al (2016); Zeng and Prasanna (2020); Yan et al (2020); Auten et al (2020); Geng et al (2020); Kiningham et al (2020) describe various other examples of GNN acceleration architectures. While these frameworks are applicable to various graph processing tasks, they may not apply to the strict latency requirements of the LHC trigger and they typically require the user to specify the design in a highly specialized format.…”

Section: Related Workmentioning

confidence: 99%

Graph Neural Networks for Charged Particle Tracking on FPGAs

Elabd,

Razavimaleki,

Huang

et al. 2021

Preprint

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The determination of charged particle trajectories in collisions at the CERN Large Hadron Collider (LHC) is an important but challenging problem, especially in the high interaction density conditions expected during the future high-luminosity phase of the LHC (HL-LHC). Graph neural networks (GNNs) are a type of geometric deep learning algorithm that has successfully been applied to this task by embedding tracker data as a graph-nodes represent hits, while edges represent possible track segments-and classifying the edges as true or fake track segments. However, their study in hardware-or software-based trigger applications has been limited due to their large computational cost. In this paper, we introduce an automated translation workflow, integrated into a broader tool called hls4ml, for converting GNNs into firmware for field-programmable gate arrays (FPGAs). We use this translation tool to implement GNNs for charged particle tracking, trained using the TrackML challenge dataset, on FPGAs with designs targeting different graph sizes, task complexites, and latency/throughput requirements. This work could enable the inclusion of charged particle tracking GNNs at the trigger level for HL-LHC experiments.

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GRIP: A Graph Neural Network Accelerator Architecture

Cited by 15 publications

References 0 publications

Sampling Methods for Efficient Training of Graph Convolutional Networks: A Survey

Sampling Methods for Efficient Training of Graph Convolutional Networks: A Survey

Characterizing the Communication Requirements of GNN Accelerators: A Model-Based Approach

Graph Neural Networks for Charged Particle Tracking on FPGAs

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