Bayesian Optimization Approach for RF Circuit Synthesis via Multitask Neural Network Enhanced Gaussian Process.
- Resource Type
- Article
- Authors
- Huang, Jiangli; Tao, Cong; Yang, Fan; Yan, Changhao; Zhou, Dian; Zeng, Xuan
- Source
- IEEE Transactions on Microwave Theory & Techniques. Nov2022, Vol. 70 Issue 11, p4787-4795. 9p.
- Subject
- *GAUSSIAN processes
*ANALOG circuits
*COMPUTER multitasking
*KERNEL functions
*INTEGRATED circuits
*TASK analysis
- Language
- ISSN
- 0018-9480
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. [ABSTRACT FROM AUTHOR]