  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.
