Automated Design of Analog Circuits Based on Parallel Trust Region Bayesian Optimization
Peng Wei Dong, Ruiyu Lyu, Chunxi Wang, Jiale Chen, Linfeng Jiang, Cunqing Lan, Zhaori Bi, Changhao Yan · 2024
Traditional optimization algorithms suffer performance decline in high-dimensional optimization problems, such as analog circuit design optimization. Adapting existing algorithms to parallel computing environments is a critical challenge. Therefore we propose a parallel Trust Region Bayesian Optimization(TuRBO) algorithm. This algorithm operates in parallel on different trust regions, utilizing a Multi-Armed Bandit algorithm for intelligent sampling to accelerate parameter optimization. Circuit experimental results demonstrate the advantages of this algorithm. Compared to Differential Evolution, Particle Swarm Optimization, Naive Bayesian, High-Dimensional Batch Bayesian Processing, and TuRBO algorithms, the circuit performance achieves improvements ranging from 3.7% to 98.2%. Compared to TuRBO, it achieves acceleration ratios in terms of iteration numbers ranging from 1.19x to 1.31x, and in terms of algorithm runtime ranging from 1.21x to 2.25x.