Title: Efficient Large Design Space Exploration and Prediction in Computer 
Microarchitectural Study

Authors: Bin Li, Lu Peng and Balachandran Ramadass

Abstract: Computer architects usually use cycle-by-cycle simulation to evaluate design 
choices and understand tradeoffs (between processor performance and power consumption) 
and interactions among design parameters. Efficiently exploring the exponential-size design 
spaces with many interacting parameters remains an open problem: the sheer number of 
experiments renders detailed simulation intractable. However, only configurations in 
a subspace can be simulated in practice due to long simulation time and limited resource, 
leading to suboptimal conclusions which might not be applied to a larg-er design space. 
In this study, we propose an automated design space exploration and prediction method 
which employs sampling technique from experiment design and machine learning, and predictive 
modeling in data mining. Results shown that our method not only produces highly 
accurate estimation for unsampled points in the design space, but also provides interpretation 
tools that help investigators to understand performance bottlenecks.