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- W1976009969 abstract "The evaluation of new processor designs is an important issue in electrical and computer engineering. Architects use simulations to evaluate designs and to understand trade-offs and interactions among design parameters. However, due to the lengthy simulation time and limited resources, it is often practically impossible to simulate a full factorial design space. Effective sampling methods and predictive models are required. In this paper, the authors propose an automated performance predictive approach which employs an adaptive sampling scheme that interactively works with the predictive model to select samples for simulation. These samples are then used to build Bayesian additive regression trees, which in turn are used to predict the whole design space. Both real data analysis and simulation studies show that the method is effective in that, though sampling at very few design points, it generates highly accurate predictions on the unsampled points. Furthermore, the proposed model provides quantitative interpretation tools with which investigators can efficiently tune design parameters in order to improve processor performance. The Canadian Journal of Statistics 38: 136–152; 2010 © 2010 Statistical Society of CanadaL'evaluation de la conception de nouveaux processeurs est une etape importante en genie electrique et informatique. Les architectes utilisent des simulations afin d'evaluer les concepts et de comprendre les compromis et les interactions entre les differents parametres du modele de conception. Cependant, a cause de temps de simulation excessif et de la limitation des ressources, il est pratiquement impossible de simuler un devis factoriel complet. Des methodes d'echantillonnage efficaces et des modeles de prediction sont requis. Dans cet article, les auteurs proposent une approche automatique pour predire la performance qui utilise un plan d'echantillonnage adaptatif interagissant avec le modele predictif pour choisir les echantillons lors de la simulation. Ces echantillons sont alors utilises pour construire des arbres de regression bayesiens additifs qui sont a leur tour utilises pour predire l'ensemble de l'espace des devis. Des analyses de vraies donnees et des etudes de simulation ont montre que cette methode est efficace. En effet, meme si l'echantillonnage est fait sur tres peu de points de devis, il genere des predictions tres precises sur les points non echantillonnes. De plus, le modele propose fournit des outils d'interpretation quantitatifs permettant aux chercheurs d'ajuster precisement les parametres du devis afin d'ameliorer les performances du processeur. La revue canadienne de statistique 38: 136–152; 2010 © 2010 Societe statistique du Canada" @default.
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- W1976009969 date "2010-01-01" @default.
- W1976009969 modified "2023-09-22" @default.
- W1976009969 title "An adaptive sampling scheme guided by BART-with an application to predict processor performance" @default.
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- W1976009969 doi "https://doi.org/10.1002/cjs.10049" @default.
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