I-GCN: A Graph Convolutional Network Accelerator with Runtime Locality Enhancement through Islandization
Tong Geng, Chunshu Wu, Yongan Zhang, Cheng Tan, Chenhao Xie, Haoran You, Martin Herbordt, Yingyan Lin, Ang Li
Abstract
Graph Convolutional Networks (GCNs) have drawn tremendous attention in the past three years. Compared with other deep learning modalities, high-performance hardware acceleration of GCNs is as critical but even more challenging. The hurdles arise from the poor data locality and redundant computation due to the large size, high sparsity, and irregular non-zero distribution of real-world graphs.
Topics & Concepts
LocalityComputer scienceGraphComputationModalitiesParallel computingTheoretical computer scienceAlgorithmSociologyPhilosophyLinguisticsSocial scienceAdvanced Graph Neural NetworksFerroelectric and Negative Capacitance DevicesAdvanced Memory and Neural Computing