Machine learning-driven optimization of circuit design

Abstract

The rapid progression of electronic systems demands more and more efficient and reliable circuit design. Optimization methods often face the complexity of modern circuits and limitations in controlling the wider design space. This chapter on circuit design explores the integration of machine learning (ML) techniques as powerful tools for enhancing optimization. We relate ML by introducing an overview of methods, which include observed education, reinforcement education, evolutionary algorithms, and Deep Wanda education, and their applications in predicting circuit performance metrics and guiding design decisions. Through various cases of teaching spread in analog, digital, and VLSI circuit design, we show how ML-powered approaches improve optimization in terms of power consumption, motion, field, and overall reliability. This chapter also discusses current challenges, such as a lack of data and model interpretation, and illuminates future directions to achieve more autonomous and intelligent circuit design workflows.