Microarchitectural Design Space Exploration Using an Architecture-Centric Approach

The microarchitectural design space of a new processor is too large for an architect to evaluate in its entirety. Even with the use of statistical simulation, evaluation of a single configuration can take excessive time due to the need to run a set of benchmarks with realistic workloads. This paper proposes a novel machine learning model that can quickly and accurately predict the performance and energy consumption of any set of programs on any microarchitectural configuration. This architecture-centric approach uses prior knowledge from off-line training and applies it across benchmarks. This allows our model to predict the performance of any new program across the entire microarchitecture configuration space with just 32 further simulations. We compare our approach to a state-of-the-art programspecific predictor and show that we significantly reduce prediction error. We reduce the average error when predicting performance from 24% to just 7% and increase the correlation coeffi...

• Type: Conference
• Year: 2007
• Where: MICRO
• Authors: Christophe Dubach, Timothy M. Jones, Michael F. P. O'Boyle
• Keywords: Design Space, Hardware, MICRO 2007, Microarchitectural Design Space, Microarchitecture Configuration Space