Late Breaking Results: Breaking Symmetry- Unconventional Placement of Analog Circuits using Multi-Level Multi-Agent Reinforcement Learning
This addresses performance issues in analog circuit design for engineers, though it is incremental as it builds on existing layout automation methods.
The paper tackles the problem of layout-dependent effects (LDEs) impacting analog circuit performance by proposing a multi-level multi-agent reinforcement learning framework to explore unconventional layouts, achieving better variation performance than state-of-the-art techniques.
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.