Fast ML-driven Analog Circuit Layout using Reinforcement Learning and Steiner Trees
<|reference_start|>Fast ML-driven Analog Circuit Layout using Reinforcement Learning and Steiner Trees: 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.<|reference_end|>
@article{basso2024fast,
    title={Fast ML-driven Analog Circuit Layout using Reinforcement Learning and
  Steiner Trees},
    author={Davide Basso, Luca Bortolussi, Mirjana Videnovic-Misic, Husni Habal},
    journal={arXiv preprint arXiv:2405.16951},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.16951},
    primaryClass={cs.LG}
}