Harvesting Design Knowledge From the Internet: High-Dimensional Performance Tradeoff Modeling for Large-Scale Analog Circuits

—Efficiently optimizing large-scale, complex analog systems requires to know the performance tradeoffs for various analog circuit blocks. In this paper, we propose a radically new approach for analog performance tradeoff modeling. Our key idea is to broadly search the rich design knowledge from the Internet, and then mathematically encode the knowledge as high-dimensional performance tradeoff curves that are referred to as Pareto fronts in the literature. Toward this goal, several novel numerical algorithms, such as sparse regression and semi-infinite programming, are developed in order to construct the high-dimensional Pareto front model while guaranteeing its monotonicity. Our numerical examples demonstrate that the proposed modeling technique can accurately capture the high-dimensional Pareto fronts for large-scale analog systems (e.g., analog-to-digital converter) while most traditional methods are limited to low-dimensional Pareto front modeling of small circuit blocks without...

• Type: Journal
• Year: 2016
• Where: TCAD
• Authors: Jun Tao, Changhai Liao, Xuan Zeng, Xin Li 0001
• Keywords: Information Technology, TCAD 2016