Late Breaking Results: Reinforcement Learning for Scalable Logic Optimization with Graph Neural Networks

Logic optimization is an NP-hard problem commonly approached through hand-engineered heuristics. We propose to combine graph convolutional networks with reinforcement learning and a novel, scalable node embedding method to learn which local transforms should be applied to the logic graph. We show that this method achieves a similar size reduction as ABC on smaller circuits and outperforms it by $1. 5 - 1. 75 \times $ on larger random graphs.