BlockGNN: Towards Efficient GNN Acceleration Using Block-Circulant Weight Matrices

Zhou, Zhe; Shi, Bizhao; Zhang, Zhe; Guan, Yijin; Sun, Guangyu; Luo, Guojie

doi:10.1109/dac18074.2021.9586181

Cited by 16 publications

(4 citation statements)

References 10 publications

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“…For instance, GNN explanations (e.g., subgraphs) can reveal which components in molecule graphs support the final biochemical functionality predictions of GNNs [56]. ASR * , Accuracy [28] Accuracy [29], Faithfulness [30] ASR * , Data Leakage [31] Group/Individual Fairness [32], [33] Standard Evaluations [34] Inference Time [35], Nodes-Per-Joule [36] are commonly used metrics for evaluating GNN explanations. In specific applications, some metrics based on domain knowledge (e.g., correlated separability [59] in computational pathology) are also used to measure explanations.…”

Section: Trustworthy Gnnsmentioning

confidence: 99%

“…Metrics. Generally, resource/efficiency-related metrics such as inference time [35], memory footprint [72], or nodes-per-joule [36] are employed to evaluate efforts on environmental wellbeing of GNNs.…”

Section: Trustworthy Gnnsmentioning

confidence: 99%

“…In this section, we review recent methods for improving the efficiency of GNNs, which is regarded as aligning GNN research with social values regarding environmental well-being. Generally, the efficiency improvement is evaluated with reference to time-related metrics (e.g., response latency or speedup rating [257], [258], throughput rating [259], [260], communication time [261]), energy-related metrics (e.g., nodes-per-Joule [36], energy consumption [262]), or resource-related metrics (e.g., memory footprint [72], cache access performance, and peak memory usage [263]). Existing methods include scalable GNN architectures and efficient data communication, model compression methods, efficient frameworks and accelerators.…”

Section: Environmental Well-being Of Gnnsmentioning

confidence: 99%

See 2 more Smart Citations

Trustworthy Graph Neural Networks: Aspects, Methods and Trends

Zhang¹,

Wu²,

Yuan³

et al. 2022

Preprint

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Graph neural networks (GNNs) have emerged as a series of competent graph learning methods for diverse real-world scenarios, ranging from daily applications like recommendation systems and question answering to cutting-edge technologies such as drug discovery in life sciences and n-body simulation in astrophysics. However, task performance is not the only requirement for GNNs. Performance-oriented GNNs have exhibited potential adverse effects like vulnerability to adversarial attacks, unexplainable discrimination against disadvantaged groups, or excessive resource consumption in edge computing environments. To avoid these unintentional harms, it is necessary to build competent GNNs characterised by trustworthiness. To this end, we propose a comprehensive roadmap to build trustworthy GNNs from the view of the various computing technologies involved. In this survey, we introduce basic concepts and comprehensively summarise existing efforts for trustworthy GNNs from six aspects, including robustness, explainability, privacy, fairness, accountability, and environmental well-being. Additionally, we highlight the intricate cross-aspect relations between the above six aspects of trustworthy GNNs. Finally, we present a thorough overview of trending directions for facilitating the research and industrialisation of trustworthy GNNs.

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Section: Trustworthy Gnnsmentioning

confidence: 99%

Section: Trustworthy Gnnsmentioning

confidence: 99%

Section: Environmental Well-being Of Gnnsmentioning

confidence: 99%

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Trustworthy Graph Neural Networks: Aspects, Methods and Trends

Zhang¹,

Wu²,

Yuan³

et al. 2022

Preprint

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“…BlockGNN ( Zhou et al, 2021 ) proposes a pipelined CirCore architecture to compute block circulant matrices efficiently. BlockGNN selected the Reddit dataset to analyze the total computation and algorithm strength of GCN, Graphsage, GAT, and G-GCN in the aggregation and combination phases, respectively.…”

Section: Fpga Based Hardware Acceleratorsmentioning

confidence: 99%

A survey of field programmable gate array (FPGA)-based graph convolutional neural network accelerators: challenges and opportunities

Yao

Tang

et al. 2022

PeerJ Computer Science

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Graph convolutional networks (GCNs) based on convolutional operations have been developed recently to extract high-level representations from graph data. They have shown advantages in many critical applications, such as recommendation system, natural language processing, and prediction of chemical reactivity. The problem for the GCN is that its target applications generally pose stringent constraints on latency and energy efficiency. Several studies have demonstrated that field programmable gate array (FPGA)-based GCNs accelerators, which balance high performance and low power consumption, can continue to achieve orders-of-magnitude improvements in the inference of GCNs models. However, there still are many challenges in customizing FPGA-based accelerators for GCNs. It is necessary to sort out the current solutions to these challenges for further research. For this purpose, we first summarize the four challenges in FPGA-based GCNs accelerators. Then we introduce the process of the typical GNN algorithm and several examples of representative GCNs. Next, we review the FPGA-based GCNs accelerators in recent years and introduce their design details according to different challenges. Moreover, we compare the key metrics of these accelerators, including resource utilization, performance, and power consumption. Finally, we anticipate the future challenges and directions for FPGA-based GCNs accelerators: algorithm and hardware co-design, efficient task scheduling, higher generality, and faster development.

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Practical Near-Data-Processing Architecture for Large-Scale Distributed Graph Neural Network

Huang

Zhang

et al. 2022

IEEE Access

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Graph Neural Networks have drawn tremendous attention in the past few years due to their convincing performance and high interpretability in various graph-based tasks like link prediction and node classification. With the ever-growing graph size in the real world, especially for industrial graphs at a billionlevel, the storage of graphs can easily consume Terabytes so that the process of GNNs has to be processed in a distributed manner. As a result, the execution could be inefficient due to the expensive cross-node communication and irregular memory access. Various GNN accelerators have been proposed for efficient GNN processing. They, however, mainly focused on small and medium-size graphs, which is not applicable to large-scale distributed graphs. In this paper, we present a practical Near-Data-Processing architecture based on a memory-pool system for large-scale distributed GNNs. We propose a customized memory fabric interface to construct the memory pool for low-latency and high throughput cross-node communication, which can provide flexible memory allocation and strong scalability. A practical Near-Data-Processing design is proposed for efficient work offloading and bandwidth utilization improvement. Moreover, we also introduce a partition and scheduling scheme to further improve performance and achieve workload balance. Comprehensive evaluations demonstrate that the proposed architecture can achieve up to 27× and 8× higher training speed compared to two state-of-the-art distributed GNN frameworks: Deep Graph Library and P 3 , respectively.INDEX TERMS Graph neural network, large-scale graph processing, memory pool, near data processing.

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BlockGNN: Towards Efficient GNN Acceleration Using Block-Circulant Weight Matrices

Cited by 16 publications

References 10 publications

Trustworthy Graph Neural Networks: Aspects, Methods and Trends

Trustworthy Graph Neural Networks: Aspects, Methods and Trends

A survey of field programmable gate array (FPGA)-based graph convolutional neural network accelerators: challenges and opportunities

Practical Near-Data-Processing Architecture for Large-Scale Distributed Graph Neural Network

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