Accelerating Computer Architecture Simulation through Machine Learning
This addresses the challenge of efficient architectural exploration for computer architects, but it is incremental as it builds on existing ML techniques for simulation acceleration.
The paper tackles the problem of slow computer architecture simulation by using machine learning to predict application performance from partial simulation data, achieving a root mean square error of less than 0.1 for IPC predictions.
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.