Fast ML-driven Analog Circuit Layout using Reinforcement Learning and Steiner Trees

2024-05-27 · Davide Basso, Luca Bortolussi, Mirjana Videnovic-Misic, Husni Habal

This paper presents an artificial intelligence driven methodology to reduce the bottleneck often encountered in the analog ICs layout phase. We frame the floorplanning problem as a Markov Decision Process and leverage reinforcement learning for automatic placement generation under established topological constraints. Consequently, we introduce Steiner tree-based methods for the global routing step and generate guiding paths to be used to connect every circuit block. Finally, by integrating these solutions into a procedural generation framework, we present a unified pipeline that bridges the divide between circuit design and verification steps. Experimental results demonstrate the efficacy in generating complete layouts, eventually reducing runtimes to 1.5% compared to manual efforts.

📄 PDF Abstract BibTeX arXiv:2405.16951

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