Accelerating Computer Architecture Simulation through Machine Learning

Wajid Ali; Ayaz Akram

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arXiv · 2402.18746

Accelerating Computer Architecture Simulation through Machine Learning

Wajid Ali·Ayaz Akram

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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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Wajid Ali, Ayaz Akram. 2024-02-28. Accelerating Computer Architecture Simulation through Machine Learning. https://arxiv.org/abs/2402.18746

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