BOXGB: Design Parameter Optimization with Systematic Integration of Bayesian Optimization and XGBoost

Finding design flow parameters that ensure a high quality of final chip is a very important task, but requires an excessive amount of effort and time. In this work, we automate this task by proposing a machine learning (ML)-based design space optimization (DSO) framework. Rather than simply applying one ML model exclusively or multiple ones in a naive manner, we develop a comprehensive chain of ML engines which is able to explore the design parameter space more economically but effectively to make a fast convergence on finding the best parameter set. Specifically, we solve the DSO problem in three steps: (1) random sampling of parameter sets and then performing design evaluation to produce an initial ML training dataset; (2) iteratively, downsizing parameter dimension through Principal Component Analysis (PCA) followed by sampling through an exploration-centric mechanism which is internally driven by Bayesian Optimization (BO) and then evaluating the sample; (3) iteratively, sampling through an exploitation-centric mechanism driven by XGBoost regression and then checking anomaly by using XGBoost classification followed by evaluating the sample if it's not anomaly. From our experiments with benchmark designs, it is shown that our approach is able to find design parameter sets which are far better than that found by the prior state-of-the-art ML-based approaches, even with fewer number of design evaluations (i.e., EDA tool runs). In addition, in comparison with the designs produced by using the default parameter setting, our DSO framework is able to improve the design PPA metrics by $5\sim 30{\%}$ I.