Accelerating Computer Architecture Simulation through Machine Learning

2024/02/28 by Ali, Wajid, Akram, Ayaz
#FOS: Computer and information sciences #Hardware Architecture (cs.AR) #Machine Learning (cs.LG)

paper · doi:10.48550/arxiv.2402.18746

Abstract

This paper presents our approach to accelerate computer architecture simulation by leveraging machine learning techniques. Traditional computer architecture simulations are time-consuming, making it challenging to explore different design choices efficiently. Our proposed model utilizes a combination of application features and micro-architectural features to predict the performance of an application. These features are derived from simulations of a small portion of the application. We demonstrate the effectiveness of our approach by building and evaluating a machine learning model that offers significant speedup in architectural exploration. This model demonstrates the ability to predict IPC values for the testing data with a root mean square error of less than 0.1.

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