A Review of Machine Learning in Logic Synthesis

Abstract

Electronic design automation tools have multiple options that need to be tuned for specific designs and technology nodes. Traditionally, the tuning process is done by teams of expert engineers and demands a large amount of computational resources.
In recent years, there has been an increased effort to apply machine learning techniques in electronic design automation problems, attempting to increase the design flow correlation and predictability, hence reducing the time spent on tuning.
In this work, we revise modern approaches in electronic design automation and machine learning techniques applied during logic synthesis.
We categorize and discuss their core technologies, such as transforming data into images.
Machine learning techniques are as good as the available data. Thus, we present existing learning datasets for logic synthesis and strategies such as data augmentation to overcome the lack of specific data for logic synthesis problems.
To cope with these problems, we discuss how research is shifting from traditional supervised learning techniques to reinforcement learning-based methods.