A comprehensive survey on graph neural network accelerators

doi:10.1007/s11704-023-3307-2

Front. Comput. Sci.

2025, Vol. 19

Issue (2) : 192104 https://doi.org/10.1007/s11704-023-3307-2

Architecture

A comprehensive survey on graph neural network accelerators

Jingyu LIU^1,², Shi CHEN^1,², Li SHEN^1,²(

)

¹. School of Computer, National University of Defense Technology, Changsha 410073, China
². Key Laboratory of Advanced Microprocessor Chips and Systems, Changsha 410073, China

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Abstract

Deep learning has gained superior accuracy on Euclidean structure data in neural networks. As a result, non-Euclidean structure data, such as graph data, has more sophisticated structural information, which can be applied in neural networks as well to address more complex and practical problems. However, actual graph data obeys a power-law distribution, so the adjacent matrix of a graph is random and sparse. Graph processing accelerator (GPA) is designed to handle the problems above. However, graph computing only processes 1-dimensional data. In graph neural networks (GNNs), graph data is multi-dimensional. Consequently, GNNs include the execution processes of both traditional graph processing and neural network, which have irregular memory access and regular computation, respectively. To obtain more information in graph data and require better model generalization ability, the layers of GNN are deeper, so the overhead of memory access and computation is considerable. At present, GNN accelerators are designed to deal with this issue. In this paper, we conduct a systematic survey regarding the design and implementation of GNN accelerators. Specifically, we review the challenges faced by GNN accelerators, and existing related works in detail to process them. Finally, we evaluate previous works and propose future directions in this booming field.

Keywords graph neural network accelerators graph convolutional networks design space exploration deep learning domain-specific architecture

Corresponding Author(s): Li SHEN

Just Accepted Date: 22 November 2023 Issue Date: 18 March 2024

Cite this article:

Jingyu LIU,Shi CHEN,Li SHEN. A comprehensive survey on graph neural network accelerators[J]. Front. Comput. Sci., 2025, 19(2): 192104.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-023-3307-2
https://academic.hep.com.cn/fcs/EN/Y2025/V19/I2/192104

Fig.1 The entire process of GNN [50]

Tab.1 Current GNN acceleration architectures

Fig.2 The design aspects of GNN accelerators

Tab.2 Execution behaviors in GNNs [49]

Tab.3 Current common GNN dataset

Fig.3 The non-zero elements of GNN adjacent matrix (a) and weight matrix (b) [26]

Fig.4 An example of inference on vertex B using a two-layer GCN. The nodeflow describes the propagation of features within a message-passing layer (MPL) [60]. (a) Input graph; (b) nodeflow; (c) inference dataflow

Fig.5 Execution time breakdown of the two phases [49]

Fig.6 Breakdown of the pipeline slots spent on retiring micro-ops or stalled by different bottlenecks during a full-batch training of GraphSAGE on a CPU [31]

Fig.7 Overview of GNNAdvisor [25]

Fig.8 A breakdown of memory usage and data similarity for different stages of the SET model when training OGB-Papers over multiple GPUs (G0, G1,...) with 16 GB of memory each [20]

Fig.9 Illustrative examples of EdgeUpdate redundancy (ER) and Aggregation redundancy (AR) [61]

Fig.10 Overview of ReGNN [61]

Fig.11 Overview of I-GCN [30]

Fig.12 Overview of GCOD [29]

Fig.13 Overview of GROW [32]

Fig.14 Overview of SGCN [33]

Fig.15 Per-minibatch scheduling between CPU and FPGA (up), and between FPGA computation modules (down) [52]

Fig.16 The number of operations for the five datasets (first layer) using the two execution orders [28]

Fig.17 FlexGraph architecture [21]

Fig.18 Execution paths of backward aggregations in two layers on the example graph [35]

Fig.19 Overview of GRIP [60]

Fig.20 The main framework of QGTC [24]

Fig.21 High-level view of the stochastic element of Degree-Quant. Masked (high in-degree) nodes, in green, operate at full precision, while unmasked nodes (red) operate at reduced precision. High in-degree nodes contribute most to poor gradient estimates, hence they are stochastically masked more often [22]

Fig.22 Multi-Granularity Quantization: (a) Component-wise, (b) Topology-aware, (c) Layer-wise, and (d) Uniform Quantization. NOTE: the same color represents the same quantization bit [23]

Fig.23 Overview of FlowGNN [65]

Fig.24 The ReRAM architecture [28]

Fig.25 The REFLIP architecture [27]

Fig.26 Comparing and contrasting the classic graph mapping and our flipped mapping for GCNs. (a) A sample graph, (b) traditional graph processing mapping scheme that maps sparse edge data into crossbar arrays, and (c) the flipped-mapping scheme in REFLIP that maps multi-dimensional vertex features into crossbar arrays. The notation

e i, j

represents an edge pointing from the vertex

i

to the vertex

j

[27]

Fig.27 Overview of ReGNN [62]

Fig.28 GCN design with the two design patterns. (a) Adjacent matrix; (b) GCN design with general MM (GEMM) crossbars; (c) GCN design with CAM crossbars and MAC crossbars [34]

Fig.29 Overview of PASGCN [34]

Fig.30 Overview of GraNDe [66]

Notations	Descriptions
$G$	A graph
$\| ? \|$	The length of a set
$V$	The set of nodes in a graph
$?$	Element-wise product
$A T$	Transpose of adjacent matrix $A$
$v i$	A node $v i ∈$ $V$
$E$	The set of edges in a graph
$N (v)$	the neighbors of node $v$
$D v$	The degrees of a node $v$
$e i j$	An edge $e i j ∈$ $E$
$N$	The number of nodes, $N = \| V \|$
$M$	The number of edges, $M = \| E \|$
$D$	The dimension of a node vector
$X$ $∈ R N × D$	The feature matrix of a graph
$x$ $∈ R N$	The feature vector of a graph when $D$ equals 1
$X i ∈ R D$	The feature vector of the node $v i$

Table A1 Symbols and definitions

Models	Combine	Aggregate
GCN	$h v l ? 1$	$∑ c u l$
GIN	$M L P l (h v l ? 1, W l)$	$(1 + ?) ? c v l + ∑ c u l$
GraphSAGE	$h v l ? 1$	$M e a n (c u l)$
GAT	$h v l ? 1$	$∑ α v, u l ? c u l$

Table A2 Computational operations on two phases of GNN

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