Abstract
Graph neural networks (GNNs) have attracted increasing interests in recent years. Due to the poor data locality and huge data movement during GNN inference, it is challenging to employ GNN to process large-scale graphs. Fortunately, processing-in-memory (PIM) architecture has been widely investigated as a promising approach to address the “Memory Wall”. In this work, we propose a PIM architecture to accelerate GNN inference. We develop an optimized dataflow to leverage the inherent parallelism of GNNs. Targeting the dataflow, we further propose a hierarchical NoC to perform concurrent data transmission. Experimental results show that our design can outperform prior works significantly.
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Acknowledgement
This work is supported by National Key Research and Development Project of China (Grant No. 2018YFB1003304) and Beijing Academy of Artificial Intelligence (BAAI).
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Wang, Z. et al. (2020). GNN-PIM: A Processing-in-Memory Architecture for Graph Neural Networks. In: Dong, D., Gong, X., Li, C., Li, D., Wu, J. (eds) Advanced Computer Architecture. ACA 2020. Communications in Computer and Information Science, vol 1256. Springer, Singapore. https://doi.org/10.1007/978-981-15-8135-9_6
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DOI: https://doi.org/10.1007/978-981-15-8135-9_6
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