A Novel and Efficient Bayesian Optimization Approach for Analog Designs with Multi-Testbench

Abstract

Analog circuits are characterized by various circuit performances obtained from multiple testbenches which need to be simulated independently. In this paper, we propose an efficient Bayesian optimization approach for multi-testbench analog circuit design. Predictive Entropy Search with Constraints (PESC) is applied for selecting the suitable testbench to simulate, and time-weighted PESC (wPESC) is also proposed considering different analysis time. Furthermore, the Feasibility Expected Improvement (FEI) acquisition function for constraints and solving a multi-modal optimal problem of FEI are proposed to improve the efficiency of exploring feasible regions. The proposed approach can gain $2.{7}\sim 3.8\times$ speedup compared with the state-of-the-art method, and achieve better optimization results.