A study of advances in machine learning-based electronic design automation

Abstract. The electronic design automation (EDA) is a convenient tool for designing integrated circuits (IC), which is employed extensively both in academic and engineering. The design of integrated circuits is conducted in accordance with a defined design flow, commonly referred to as the chip design flow, which can be divided into two distinct parts, the front-end design and the back-end design. Following a long period of evolution, the chip design flow of EDA has been gradually improved. Besides, it achieved some accomplishments during this period. However, with the growing demand of ICs, especially Very Large-Scale Integration Circuit (VLSI), the existing EDA is not adequate for the requirement. In addition, the EDA technology has been so developed that it is relatively less flexible. In this context, concerns about the future of EDA have recently emerged. In response to this challenge, research has mentioned that machine learning methods (ML methods) can improve the functionality of EDA. The ML method covers most of steps of EDAs design flow, especially back-end design. The machine learning-based electronic design automation is still in its infancy, which is presented with a multitude of challenges. Therefore, the paper explores the development of EDA by reviewing and organizing the related literature, and summarizes the application of ML methods in EDA, thereby providing the future development trend of ML-based EDA.