DRiLLS: Deep Reinforcement Learning for Logic Synthesis

Logic synthesis requires extensive tuning of the synthesis optimization flow where the quality of results (QoR) depends on the sequence of optimizations used. The authors propose a novel reinforcement learning-based methodology that navigates the optimization space without human intervention.

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Abdelrahman Hosny, Soheil Hashemi, Mohamed Shalan, Sherief Reda (2024). Dataset: DRiLLS: Deep Reinforcement Learning for Logic Synthesis. https://doi.org/10.57702/mlm7cemn

DOI retrieved: December 16, 2024

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Created December 16, 2024
Last update December 16, 2024
Defined In https://doi.org/10.48550/arXiv.1911.04021
Author Abdelrahman Hosny
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Soheil Hashemi
Mohamed Shalan
Sherief Reda
Homepage https://github.com/scale-lab/DRiLLS