GNN adaptive training paper is accepted by DAC 2024!

👏 Paper title: GNNavigator: Towards Adaptive Training of Graph Neural Networks via Automatic Guideline Exploration. This work addresses the challenge of balancing training runtime, memory consumption, and accuracy in Graph Neural Networks (GNNs), which have seen significant success in various applications. GNNavigator introduces an adaptive GNN training configuration optimization framework. By leveraging a unified software-hardware co-abstraction, a novel GNN training performance model, and an effective design space exploration strategy, GNNavigator meets the diverse requirements of GNN applications. [related project]