Machine Learning for Microarchitecture Power Modeling and Design Space Exploration：A Survey

Microarchitecture design is a key stage of processor development. It is at the upper level of the entire design flow and directly affects core metrics such as performance, power consumption, and cost. Over the past few decades, new microarchitecture solutions, coupled with advances in semiconductor manufacturing, have enabled newer generations of processors to achieve higher performance, lower power consumption and cost. However, as chip design enters the post-Moore era, the dividends from the evolution of semiconductor technology are increasingly limited, and power consumption has become a major challenge for energy-efficient processor design. Meanwhile, modern processors are becoming more complex in architecture and the design space is larger, requiring designers to make accurate design metrics tradeoffs to achieve the most desirable microarchitecture design. Moreover, the existing stage-by-stage decomposition of the development and validation flow is extremely lengthy and time-consuming, and it is difficult to achieve global energy efficiency optimization. Therefore, how to perform accurate and efficient power estimation and design space exploration at the microarchitecture design stage becomes a key issue. To tackle these challenges, machine learning has been introduced into the microarchitecture design process, providing efficient and accurate solutions for microarchitecture modeling and optimization. We firstly introduce the main design flow of processors, microarchitecture design and its major challenges, then amplify machine learning-assisted integrated circuit design, which focuses on research advances in the use of machine learning techniques to assist microarchitecture power modeling and design space exploration, and finally conclude with a summary and outlook.