TT-GNN: Efficient On-Chip Graph Neural Network Training via Embedding Reformation and Hardware Optimization
Zheng Qu, Dimin Niu, Shuangchen Li, Hongzhong Zheng, Yuan Xie · 2023
Training Graph Neural Networks on large graphs is challenging due to the need to store graph data and move them along the memory hierarchy. In this work, we tackle this by effectively compressing graph embedding matrix such that the model training can be fully enabled with on-chip compute and memory resources. Specifically, we leverage the graph homophily property and consider using Tensor-train to represent the graph embedding. This allows nodes with similar neighborhoods to partially share the feature representation.