Accelerating Computer Architecture Simulation through Machine Learning

• URL: http://arxiv.org/abs/2402.18746v1
• Date: Wed, 28 Feb 2024 23:00:57 GMT
• Title: Accelerating Computer Architecture Simulation through Machine Learning
• Authors: Wajid Ali and Ayaz Akram
• Abstract summary: This paper presents our approach to accelerate computer architecture simulation by leveraging machine learning techniques. Our proposed model utilizes a combination of application features and micro-architectural features to predict the performance of an application. We demonstrate the effectiveness of our approach by building and evaluating a machine learning model that offers significant speedup in architectural exploration.
• Score: 0.07252027234425332
• License: http://creativecommons.org/licenses/by/4.0/
• 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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