An Efficient Bayesian Optimization Approach for Automated Optimization of Analog Circuits
An Efficient Bayesian Optimization Approach for Automated Optimization of Analog Circuits
复制标题
一种用于模拟电路自动优化的高效贝叶斯优化方法
DOI：
10.1109/tcsi.2017.2768826
复制
发表时间：
2018-06-01
影响因子：
5.1
通讯作者：
Zhou, Dian
中科院分区：
文献类型：
--
作者：
Lyu, Wenlong;Xue, Pan;Zhou, Dian
计算密集型的电路仿真使得模拟电路尺寸设计对于大规模/复杂的模拟/RF电路具有挑战性。最近提出了一种贝叶斯优化方法，用于求解目标函数或约束中计算量大的黑箱函数的优化问题。在本文中，我们提出了一个加权的预期改善为基础的贝叶斯优化方法的自动模拟电路尺寸。高斯过程（GP）被用作电路性能的在线代理模型。在优化过程中，选择期望改进作为获取函数，以平衡探索和开发。预期的改善是由满足约束的概率加权。在本文中，我们提出了一个完整的贝叶斯优化框架的优化模拟电路的约束条件首次。现有的基于GP模型的模拟电路优化方法要么将GP模型作为离线模型，要么作为进化算法的辅助。我们还扩展了贝叶斯优化算法来处理多目标优化问题。与本文中列出的最先进的方法相比，所提出的贝叶斯优化方法取得了更好的优化结果，显着减少模拟次数。
The computation-intensive circuit simulation makes the analog circuit sizing challenging for large-scale/complicated analog/RF circuits. A Bayesian optimization approach has been proposed recently for the optimization problems involving the evaluations of black-box functions with high computational cost in either objective functions or constraints. In this paper, we propose a weighted expected improvement-based Bayesian optimization approach for automated analog circuit sizing. Gaussian processes (GP) are used as the online surrogate models for circuit performances. Expected improvement is selected as the acquisition function to balance the exploration and exploitation during the optimization procedure. The expected improvement is weighted by the probability of satisfying the constraints. In this paper, we propose a complete Bayesian optimization framework for the optimization of analog circuits with constraints for the first time. The existing GP model-based optimization methods for analog circuits take the GP models as either offline models or as assistance for the evolutionary algorithms. We also extend the Bayesian optimization algorithm to handle multi-objective optimization problems. Compared with the state-of-the-art approaches listed in this paper, the proposed Bayesian optimization method achieves better optimization results with significantly less number of simulations.