Accelerating Computer Architecture Simulation through Machine Learning
Wajid Ali Ayaz Akram
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.