Abstract: Central processing unit is the most important computing infrastructure nowadays. To maximize the profit, architects design the processor microarchitecture by trading-off multiple objectives including performance, power, and area. However, because of the tremendous instructions of workloads running on the processors, the evaluation of individual microarchitecture design point costs minutes to hours. Furthermore, the design space of the microarchitecture is huge, which results that the exploration of comprehensive design space is unrealistic. Therefore, many machine-learning-assisted design space exploration acceleration methods are proposed to reduce the size of evaluated design space or accelerate the evaluation of a design point. However, a comprehensive survey summarizing and systematically classifying recent acceleration methods is missing. This survey paper systematically summarizes and classifies the five kinds of acceleration methods for the design space exploration of the processor microarchitecture, including the workload selection of software design space, the partial simulation of workload instructions, the design point selection, the simulation tools, and the performance models. This paper systematically compares the similarities and differences between papers in the acceleration methods, and covers the complete exploration process from the software workload selection to the hardware microarchitecture design. Finally, the research direction is summarized, and the future development trend is discussed.