An optimization-based approach to automated design

TL;DR

This paper introduces a model-based, automated bottom-up design approach for electrical circuits, utilizing optimization algorithms to efficiently handle combinatorial complexity and improve design process automation.

Contribution

It presents two novel algorithms combining continuous optimization and genetic methods to automate electrical circuit design, addressing combinatorial explosion issues.

Findings

Algorithms effectively optimize electrical circuit designs.

Time complexity is reduced through model reconstruction and topology simplification.

Demonstrated success on various electrical circuit design problems.

Abstract

We propose a model-based, automated, bottom-up approach for design, which is applicable to various physical domains, but in this work we focus on the electrical domain. This bottom-up approach is based on a meta-topology in which each link is described by a universal component that can be instantiated as basic components (e.g., resistors, capacitors) or combinations of basic components via discrete switches. To address the combinatorial explosion often present in mixed-integer optimization problems, we present two algorithms. In the first algorithm, we convert the discrete switches into continuous switches that are physically realizable and formulate a parameter optimization problem that learns the component and switch parameters while inducing design sparsity through an $L_1$ regularization term. The second algorithm uses a genetic-like approach with selection and mutation steps guided by ranking of requirements costs, combined with continuous optimization for generating optimal parameters. We improve the time complexity of the optimization problem in both algorithms by reconstructing the model when components become redundant and by simplifying topologies through collapsing components and removing disconnected ones. To demonstrate the efficacy of these algorithms, we apply them to the design of various electrical circuits.