DPBO: Dynamic Domain Decomposition-based Parallel Bayesian Optimization for Large-scale Analog Circuit Sizing

Aidong Zhao, Tianchen Gu, Zhaori Bi, Fan Yang, Changhao Yan, Xuan Zeng, Zixiao Lin, Wenchuang Walter Hu, Dian Zhou

Abstract: Bayesian optimization (BO) is an efficient global optimization method for expensive black-box functions, but the expansion for high-dimensional problems and large sample budgets still remains a severe challenge. In order to extend BO for large-scale analog circuit synthesis, a novel computationally efficient parallel BO method, D3PBO, is proposed for high-dimensional problems in this work. We introduce the dynamic domain decomposition method based on maximum variance between clusters. The search space is decomposed into subdomains progressively to limit the maximal number of observations in each domain. The promising domain is explored by multi-trust region-based batch BO with the local Gaussian process (GP) model. As the domain decomposition progresses, the basin-shaped domain is identified using a GP-assisted quadratic regression method and exploited by the local search method BOBYQA to achieve a faster convergence rate. The time complexity of D3PBO is constant for each iteration. Experiments demonstrate that D3PBO obtains better results with significantly less runtime consumption compared to state-of-the-art methods. For the circuit optimization experiments, D3PBO achieves up to 10× runtime speedup compared to TuRBO with better solutions.