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

• URL: http://arxiv.org/abs/2104.06214v1
• Date: Tue, 13 Apr 2021 14:09:22 GMT
• Title: BlockGNN: Towards Efficient GNN Acceleration Using Block-Circulant Weight Matrices
• Authors: Zhe Zhou, Bizhao Shi, Zhe Zhang, Yijin Guan, Guangyu Sun, Guojie Luo
• Abstract summary: Graph Neural Networks (GNNs) are state-of-the-art algorithms for analyzing non-euclidean graph data. How to inference GNNs in real time has become a challenging problem for some resource-limited edge-computing platforms. We propose BlockGNN, a software- hardware co-design approach to realize efficient GNN acceleration.
• Score: 9.406007544032848
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, Graph Neural Networks (GNNs) appear to be state-of-the-art algorithms for analyzing non-euclidean graph data. By applying deep-learning to extract high-level representations from graph structures, GNNs achieve extraordinary accuracy and great generalization ability in various tasks. However, with the ever-increasing graph sizes, more and more complicated GNN layers, and higher feature dimensions, the computational complexity of GNNs grows exponentially. How to inference GNNs in real time has become a challenging problem, especially for some resource-limited edge-computing platforms. To tackle this challenge, we propose BlockGNN, a software-hardware co-design approach to realize efficient GNN acceleration. At the algorithm level, we propose to leverage block-circulant weight matrices to greatly reduce the complexity of various GNN models. At the hardware design level, we propose a pipelined CirCore architecture, which supports efficient block-circulant matrices computation. Basing on CirCore, we present a novel BlockGNN accelerator to compute various GNNs with low latency. Moreover, to determine the optimal configurations for diverse deployed tasks, we also introduce a performance and resource model that helps choose the optimal hardware parameters automatically. Comprehensive experiments on the ZC706 FPGA platform demonstrate that on various GNN tasks, BlockGNN achieves up to $8.3\times$ speedup compared to the baseline HyGCN architecture and $111.9\times$ energy reduction compared to the Intel Xeon CPU platform.

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