Rare Event Detection by Acquisition-Guided Sampling

Abstract

Motivated by the challenges in detecting extremely rare failures for sophisticated specifications in circuit design, we consider the problem of detecting regions of interest (ROIs) that consist of specifications with the value of a complex target function for the system performance being below or above a certain pre-specified threshold. Though Bayesian optimization (BO) has been applied to this problem, it is not effective in identifying multiple ROIs as it was originally designed for global optimization and tends to focus on searching the area where the global optimum is most likely to be. In this work, we propose a sampling strategy for fast ROI detection within a limited number of target function evaluations. The sampling distribution is designed so that the probability of a specification being sampled is proportional to the corresponding value of the acquisition function. Such an acquisition-guided sampling algorithm promotes a wider search of the sample space and a simpler incorporation of different criteria to determine the specifications to be evaluated next. To further improve the performance, we propose a new design of the acquisition function and two modifications of existing acquisition functions. Numerical studies on synthetic functions and a real-world circuit design application demonstrate that the proposed method can enjoy a stronger exploration ability provided by sampling and achieve faster ROI detection with higher coverage. Note to Practitioners—This study considers the extremely rare failure detection in automated circuit design, fabrication, packaging, and verification. Obtaining enough observations of interest within a given budget of evaluations is challenging due to the scarcity of extremely rare failures. Bayesian optimization (BO) has been adopted to tackle this problem, but it may not achieve satisfactory coverage of multiple failure regions, since its goal is to find the global optimum of the target function. In this paper, we propose a sampling-based rare event detection strategy tailored to efficiently detect regions of interest (ROIs) with high coverage, along with newly designed acquisition functions incorporating the pre-specified threshold and ideas of experimental design. This sampling strategy has greater robustness to the choice of acquisition function. Also, since multiple queries can be easily obtained through sampling, various criteria can be easily incorporated for determining the next batch of evaluation specifications without much increase in computational complexity.