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.
@article{arxiv.2402.18746,
title = {Accelerating Computer Architecture Simulation through Machine Learning},
author = {Wajid Ali and Ayaz Akram},
journal= {arXiv preprint arXiv:2402.18746},
year = {2024}
}