Accelerating Graph Neural Network Training on ReRAM-Based PIM Architectures via Graph and Model Pruning
Chukwufumnanya Ogbogu, Aqeeb Iqbal Arka, Lukas Pfromm, Biresh Kumar Joardar, Janardhan Rao Doppa, Krishnendu Chakrabarty, Partha Pratim Pande · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · 2022
Graph neural networks (GNNs) are used for predictive analytics on graph-structured data, and they have become very popular in diverse real-world applications. Resistive random-access memory (ReRAM)-based PIM architectures can accelerate GNN training. However, GNN training on ReRAM-based architectures is both compute- and data intensive in nature. In this work, we propose a framework calledSlimGNNthat synergistically combines both graph and model pruning to accelerate GNN training on ReRAM-based architectures. The proposed framework reduces the amount of redundant information in both the GNN model and input graph(s) to streamline the overall training process. This enables fast and energy-efficient GNN training on ReRAM-based architectures. Experimental results demonstrate that using this framework, we can accelerate GNN training by up to$ {4}. {5} {\times }$while using$ {6}. {6} {\times }$less energy compared to the unpruned counterparts.