BBGP-sDFO: Batch Bayesian and Gaussian Process Enhanced Subspace Derivative Free Optimization for High-Dimensional Analog Circuit Synthesis

In this paper, we propose a novel Batch Bayesian and Gaussian Process enhanced Subspace Derivative Free Optimization method to solve high-dimensional and simulation-expensive analog circuit optimization problems. The existing optimization methods, such as Bayesian Optimization and Trust Region based Derivative Free Optimization, suffer from under-fitting surrogate models in high-dimensional problems, which leads to inefficient optimization and sub-optimal solutions. To address this issue, we propose a novel approach that integrates a batch Bayesian querying strategy for exploring the global design space and a Gaussian process enhanced subspace derivative free optimization method for exploiting promising regions in effective low-dimensional subspace. The Gaussian process is used to approximate the gradient pattern for subspace establishment, significantly enhancing the simulation efficiency. The selection of promising regions is based on an innovative region acquisition function that estimates the weighted local expected improvement. The effectiveness of the proposed method is demonstrated on real-life analog circuits, achieving 2.05×~17.65× simulation number speedup and 1.37×~16.11× runtime speedup compared with the state-of-the-art optimization methods.