Multi-Armed Bandits-Based Exploring and Exploiting the High-Dimensional Design Space for Analog Circuit with Adjoint Sensitivity

The continuous expansion of the scale of analog circuits poses significant challenges to the automation of circuit design. This is mainly reflected in the longer simulation time and insufficient exploration of the design space. Traditional optimization algorithms focus on finding optimal points, often overlooking the simulation cost. In this work, we frame the original problem as a multi-armed bandit problem. By applying a Thompson sampling strategy, we achieve a balance between global exploration and local exploitation. During the local exploitation, we propose a sensitivity-guided method ensuring rapid convergence. Experimental results demonstrate that our approach outperforms state-of-the-art techniques, achieving a 5.27x speedup and better global exploratory capabilities.