D3PBO: Dynamic Domain Decomposition based Parallel Bayesian Optimization for Large-scale Analog Circuit Sizing

Abstract

Bayesian optimization (BO) is an efficient global optimization method for expensive black-box functions. Whereas, 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 faster convergence rate. The time complexity of D³PBO is constant for each iteration. Experiments demonstrate that D³PBO obtains better results with significant 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.