Bayesian Optimization Approach for RF Circuit Synthesis via Multitask Neural Network Enhanced Gaussian Process

Abstract

An RF integrated circuit design heavily relies upon experienced experts to iteratively tune the circuit parameters. A Bayesian optimization (BO) method is explored in existing works for automated analog and RF circuit synthesis. The overall performance can be further improved by constructing a model to exploit the correlation among different circuit specifications. In this article, we propose a BO approach for RF circuit synthesis via a multitask neural network enhanced Gaussian process (MTNN-GP). We present a novel multioutput GP model, in which the kernel functions of multiple outputs are constructed from a multitask neural network with shared hidden layers and task-specific layers. Therefore, the correlation between the outputs can be captured by the shared hidden layers. Our proposed MTNN-GP-based BO method is compared with several state-of-the-art BO methods on three real word RF circuits and achieves best performance. The experimental results demonstrate the effectiveness and efficiency of our proposed method.