Co-Exploration of Graph Neural Network and Network-on-Chip Design Using AutoML

Recently, Graph Neural Networks (GNNs) have exhibited high efficiency in several graph-based machine learning tasks. Compared with the neural networks for computer vision or speech tasks (e.g., Convolutional Neural Networks), GNNs have much higher requirements on communication due to the complicated graph structures; however, when applying GNNs for real-world applications, say in recommender systems (e.g. Uber Eats), it commonly has the real-Time requirements. To deal with the tradeoff between the complicated architecture and the high-demand timing performance, both GNN architecture and hardware accelerator need to be optimized. Network-on-Chip (NoC), derived for efficiently managing the high-volume of communications, naturally becomes one of the top candidates to accelerate GNNs. However, there is a missing link between the optimize of GNN architecture and the NoC design. In this work, we present an AutoML-based framework GN-NAS, aiming at searching for the optimum GNN architecture, which can be suitable for the NoC accelerator. We devise a robust reinforcement learning based controller to validate the retained best GNN architectures, coupled with a parameter sharing approach, namely ParamShare, to improve search efficiency. Experimental results on four graph-based benchmark datasets, Cora, Citeseer, Pubmed and Protein-Protein Interaction show that the GNN architectures obtained by our framework outperform that of the state-of-The-Art and baseline models, whilst reducing model size which makes them easy to deploy onto the NoC platform.