Look Before You Leap: A Self-Review Bayesian Optimization Method for Constrained High-Dimensional Design Space Exploration

The parameterizable and synthesizable RISC-V processors enable the automatic generation of customized CPU cores through EDA tools. However, current methods often explore the extensive design space with significant model errors while neglecting design constraints, which are critical for practical implementations. To address these limitations, we propose a Self-Review Bayesian Optimization method (SRBO). This method integrates a teacher-student paradigm within a local Bayesian optimization framework to reduce model errors and enhance exploration efficiency. Additionally, it employs deep ensembles for effective constraint handling. Experimental results demonstrate that our approach outperforms state-of-the-art methods within a limited time budget, significantly enhancing exploration efficiency.