EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks
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Inspired by the great success of convolutional neural networks on structural data like videos and images, graph neural network (GNN) emerges as a powerful approach to process non-euclidean data structures and has been proved powerful in various application domains such as social network, e-commerce, and knowledge graph. However, such graph data maintained in IT companies can be extremely large and sparse, thus employing GNNs to deal with them requires substantial computational power and memory bandwidth, which induces the considerable energy and resources cost spent on general-purpose CPUs and GPUs. In addition, GNN operating on irregular graphs can hardly be fitted to the conventional neural network accelerators or graph processors, which are not designed to support the computation abstraction of GNNs. This work presents a specialized accelerator architecture, EnGN, to enable high-throughput and energy-efficient processing of large-scale graph neural networks. The proposed EnGN is designed to accelerate the three key stages of GNN propagation, which is abstracted as common computing patterns shared by typical GNNs. To support the key stages simultaneously, we propose the ring-edge-reduce(RER) dataflow that tames the poor locality of sparsely-and-randomly connected vertices, and the RER PE-array to practice RER dataflow. In addition, we utilize a graph tiling strategy to fit large graphs into EnGN and make the best use of the hierarchical on-chip buffers through adaptive computation reordering and tile scheduling. The experiments on representative GNN models with the input of realistic graphs shows that EnGN achieves 303.45x and 4.44x performance speedup while consuming 1370.52x and 93.73x less energy on average when compared to the CPU and GPU baselines empowered by the state-of-the-art software frameworks, respectively.
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