Efficiently exploring architectural design spaces via predictive modeling

By Engin Ipek, Sally A. McKee, R. Caruana, Bronis R. de Supinski, Martin Schulz. ASPLOS XII, 2006

393 citations recorded by the source corpus.

Subfields: Building and Construction, Computer Graphics and Computer-Aided Design, Geology

View Paper: https://www.semanticscholar.org/paper/e9cc8f7ed9bf1ddc352450f4085d978ccefdc3b9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Benjamin C. Lee
2. David M. Brooks
3. Onur Mutlu
4. Eva García-Martín
5. Karan Singh

Citations in the corpus

• Accurate and efficient regression modeling for microarchitectural performance and power prediction (2006, background): “, consider predicting performance of memory, core and CMP design spaces with artificial neural networks (ANN) [3].”
• Informed Microarchitecture Design Space Exploration Using Workload Dynamics (2007, neutral): “Previous work [8, 10, 12] shows that neural networks can accurately predict aggregated workload behavior during design space exploration.”