Effcient approaches to overcome non-convexity issues in analog design automation

S. Maji, P. Mandal
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引用次数: 7

Abstract

We propose two approaches to overcome limitation of convex programming technique for analog design automation. Analog design performance constraints are cast in posynomial inequality format for suitability into convex optimization based application. But in most cases original equations are not in posynomial so, either they are deliberately modeled or approximated. This leads to inaccuracy. Our first approach is based on exploiting apparent benefit of convex programming based global optimizer but still making use of highly accurate non-posynomial or signomial model. To achieve that, we combine both global and local optimizer. Global optimizer handles less accurate posynomial equation ensuring global optimality. And the initial guess obtained therefore is used by local optimizer to handle accurate signomial model. Second approach demonstrates that the targeted region to be modeled for circuit sizing can be reduced which invariably leads to better model accuracy. We develop low dropout regulator (LDO) performance metrics in posynomial and signomial format for use in the proposed methodology.
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模拟设计自动化中克服非凸性问题的有效方法
我们提出了两种方法来克服凸编程技术在模拟设计自动化中的局限性。模拟设计性能约束以多项式不等式形式转换为基于凸优化的应用。但在大多数情况下,原始方程不是多项式的,所以它们要么是故意建模的,要么是近似的。这会导致不准确。我们的第一种方法是利用凸规划全局优化器的明显优势,但仍然使用高度精确的非多项式或符号模型。为了实现这一点,我们结合了全局优化器和局部优化器。全局优化器处理不太精确的多项式方程,确保全局最优性。局部优化器利用得到的初始猜测值来处理精确的信号模型。第二种方法表明,为电路尺寸建模的目标区域可以减少,这总是导致更好的模型精度。我们开发了低dropout调节器(LDO)性能指标在拟提出的方法中使用的多项式和信号格式。
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