Machine learning for engineering

Applying machine learning techniques to solve production problems within electronic design automation is complex. This is because production engineering applications have accuracy, scalability, complexity, verifiability, and usability requirements that are not met by traditional machine learning approaches. These additional challenges are often not well understood or adequately solved in practice, which causes production machine learning approaches to fail. This invited paper examines these engineering-specific challenges and presents some effective solutions based on Solido's experience developing a suite of successful applied machine learning solutions for EDA over the past twelve years.