Automatic Parameter Tuning System under Multi-Threading Nondeterminism

With the expansion of design scales, the surge of adjustable parameters for electronic design automation (EDA) tools makes manually tuning parameters become time-consuming and challenging. Recent works employ multi-objective Bayesian optimization (BO) for automatic parameter tuning. However, non-deterministic design results of EDA tools undermine the effectiveness of BO. The existing forking technique, which can obtain reliable results by conducting design in parallel with identical parameter inputs, requires extra computational resources and runtime. To address results nondeterminism under multi-threading mode, our proposed BO framwork employs a noisy expected hypervolume improvement (NEHVI) acquisition function to conduct parameter tuning. Experimental results demonstrate that our framework achieves superior optimization results with faster convergence compared to state-of-the-art works.