用多智能体强化学习打破对称布局，优化模拟电路抗变异性能。
Late Breaking Results: Breaking Symmetry- Unconventional Placement of Analog Circuits using Multi-Level Multi-Agent Reinforcement Learning
- 分层级多智能体强化学习，探索非传统版图布局空间。
- 相比现有方法，显著提升电路在工艺波动下的性能稳定性。
- 首次将多智能体RL用于模拟电路布局自动化，适合芯片设计工程师。
布局依赖效应（LDEs）严重影响模拟电路性能。传统设计依赖对称布局以缓解工艺变异，但因效应非线性，效果有限。本文提出一种目标驱动的分层级多智能体Q-learning框架，探索模拟版图的非常规设计空间，显著优于当前最优布局技术。该方法首次将多智能体强化学习应用于模拟电路布局自动化，与基于模拟退火的非机器学习方法对比，验证了其有效性。
原文摘要 · Abstract (English)
Layout-dependent effects (LDEs) significantly impact analog circuit performance. Traditionally, designers have relied on symmetric placement of circuit components to mitigate variations caused by LDEs. However, due to non-linear nature of these effects, conventional methods often fall short. We propose an objective-driven, multi-level, multi-agent Q-learning framework to explore unconventional design space of analog layout, opening new avenues for optimizing analog circuit performance. Our approach achieves better variation performance than the state-of-the-art layout techniques. Notably, this is the first application of multi-agent RL in analog layout automation. The proposed approach is compared with non-ML approach based on simulated annealing.
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