Accelerating Computer Architecture Simulation through Machine Learning
<|reference_start|>Accelerating Computer Architecture Simulation through Machine Learning: 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.<|reference_end|>
@article{ali2024accelerating,
    title={Accelerating Computer Architecture Simulation through Machine Learning},
    author={Wajid Ali and Ayaz Akram},
    journal={arXiv preprint arXiv:2402.18746},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.18746},
    primaryClass={cs.AR cs.LG}
}