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

This work entails the design of an Auto-ML based framework called GN-NAS to search for the optimal GNN architectures within the parameter search space such that they are also implementable on the NoC platform to analyze HW specifications. The designed GN-NAS framework consists of 3 parts: Parameter search space, GNAS evaluator and NoC platform. The designed GN-NAS controller has 3 phases: GNN architecture exploration, RL training and Parameter modification. We have also proposed a parameter sharing strategy called ?ParamShare? which ensures that already trained weights in the parent architecture are transferred to the child architecture. With the datasets, we consider the citation graph datasets (Cora, Citeseer and Pubmed) and PPI which we evaluate under transductive and inductive settings respectively. Based on our experimental results, GN-NAS models achieves state-of-the-art performance for both transductive and inductive learning. We also observe that GNN models with ParamShare have smaller model sizes as compared to models without ParamShare. This shows that our designed GN-NAS framework can achieve a better trade-of between performance and model sizes. https://dl.acm.org/doi/10.1145/3453688.3461741#GLSVLSI21-vlsi14s.mp4