1. Fast ML-driven Analog Circuit Layout using Reinforcement Learning and Steiner Trees
- Author
-
Basso, Davide, Bortolussi, Luca, Videnovic-Misic, Mirjana, and Habal, Husni
- Subjects
Computer Science - Machine Learning - Abstract
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., Comment: 4 pages, 3 figures, accepted by SMACD 2024 conference
- Published
- 2024