High Dimensional Optimization for Electronic Design

Abstract

Bayesian optimization (BO) samples points of interest to update a surrogate model for a blackbox function. This makes it a powerful technique to optimize electronic designs which have unknown objective functions and demand high computational cost of simulation. Unfortunately, Bayesian optimization suffers from scalability issues, e.g., it can perform well in problems up to 20 dimensions. This paper addresses the curse of dimensionality and proposes an algorithm entitled Inspection-based Combo Random Embedding Bayesian Optimization (IC-REMBO). IC-REMBO improves the effectiveness and efficiency of the Random EMbedding Bayesian Optimization (REMBO) approach, which is a state-of-the-art high dimensional optimization method. Generally, it inspects the space near local optima to explore more points near local optima, so that it mitigates the over-exploration on boundaries and embedding distortion in REMBO. Consequently, it helps escape from local optima and provides a family of feasible solutions when inspecting near global optimum within a limited number of iterations. The effectiveness and efficiency of the proposed algorithm are compared with the state-of-the-art REMBO when optimizing a mmWave receiver with 38 calibration parameters to meet 4 objectives. The optimization results are close to that of a human expert. To the best of our knowledge, this is the first time applying REMBO or inspection method to electronic design.