Efficient system design space exploration using machine learning techniques

Berkin Ozisikyilmaz*, Gokhan Memik, Alok Nidhi Choudhary

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

28 Scopus citations


Computer manufacturers spend a huge amount of time, resources, and money in designing new systems and newer configurations, and their ability to reduce costs, charge competitive prices and gain market share depends on how good these systems perform. In this work, we develop predictive models for estimating the performance of systems by using performance numbers from only a small fraction of the overall design space. Specifically, we first develop three models, two based on artificial neural networks and another based on linear regression. Using these models, we analyze the published Standard Performance Evaluation Corporation (SPEC) benchmark results and show that by using the performance numbers of only 2% and 5% of the machines in the design space, we can estimate the performance of all the systems within 9.1% and 4.6% on average, respectively. Then, we show that the performance of future systems can be estimated with less than 2.2% error rate on average by using the data of systems from a previous year. We believe that these tools can accelerate the design space exploration significantly and aid in reducing the corresponding research/development cost and timeto-market.

Original languageEnglish (US)
Title of host publicationProceedings of the 45th Design Automation Conference, DAC
Number of pages4
StatePublished - Sep 17 2008
Event45th Design Automation Conference, DAC - Anaheim, CA, United States
Duration: Jun 8 2008Jun 13 2008

Publication series

NameProceedings - Design Automation Conference
ISSN (Print)0738-100X


Other45th Design Automation Conference, DAC
Country/TerritoryUnited States
CityAnaheim, CA


  • Design space
  • Machine learning
  • Performance prediction

ASJC Scopus subject areas

  • Hardware and Architecture
  • Control and Systems Engineering


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