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Reinforcement Learning for Scalable Logic Optimization with Graph Neural Networks

arXiv 2105.01755 ↗First posted 4 May 2021cs.LGcited by 0

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Abstract (arXiv)

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 1.5-1.75x on larger random graphs.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 2 of its 7 references that are in the map. First posted on arXiv 4 May 2021.

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2 of 7 references.

References · 2

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2019DRiLLS: Deep Reinforcement Learning for Logic Synthesiscited by 47

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