MARIO: A Superadditive Multi-Algorithm Interworking Optimization Framework for Analog Circuit Sizing

Numeric optimization methods are widely utilized to tackle complex analog circuit sizing problems, where the challenges include expensive simulations, non-linearity, and high parameter dimensionality. However, the diverse characteristics exhibited by different circuits result in varied optimization landscapes, making it difficult to identify a single algorithm that consistently outperforms others across all problems. In this paper, we introduce a multi-algorithm interworking optimization framework, which achieves optimization superadditivity based on a pool of member algorithms and a powerful algorithm-interworking protocol. We propose a computing resource reallocation method, which employs multitask Gaussian process regression and portfolio optimization techniques, leading to flexible and prudent online adaption of member algorithms. To efficiently utilize the computing resources for local exploitation, an evaluation data broadcast strategy enables cooperativeness across member algorithms. Besides, algorithms with different modeling overheads are integrated time-adaptively via an asynchronous parallelization mechanism. Comparative experiments against state-of-the-art algorithmcombining tools and optimization algorithms demonstrate the superiority of the proposed optimization framework.