Late Breaking Results: Breaking Symmetry- Unconventional Placement of Analog Circuits using Multi-Level Multi-Agent Reinforcement Learning

• URL: http://arxiv.org/abs/2503.22958v3
• Date: Thu, 10 Apr 2025 19:42:17 GMT
• Title: Late Breaking Results: Breaking Symmetry- Unconventional Placement of Analog Circuits using Multi-Level Multi-Agent Reinforcement Learning
• Authors: Supriyo Maji, Linran Zhao, Souradip Poddar, David Z. Pan,
• Abstract summary: We propose an objective-driven, multi-level, multi-agent Q-learning framework to explore unconventional design space of analog layout.<n>Our approach achieves better variation performance than the state-of-the-art layout techniques.
• Score: 4.684022970694239
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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