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Applying Perceptrons to Speculation in Computer Architecture

  • Couverture cartonnée
  • 256 Nombre de pages
Revision with unchanged content. Modern microprocessors make use of speculation, or predictions about future program behavior, to ... Lire la suite
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Description

Revision with unchanged content. Modern microprocessors make use of speculation, or predictions about future program behavior, to optimize the execution of programs. Perceptrons are simple neural networks that can be highly useful in speculation for their ability to examine larger quantities of available data than more commonly used approaches, and identify which data lead to accurate results. This work first studies how perceptrons can be made to predict accurately when they directly replace the traditional pattern table predictor. Different training me thods, perceptron topologies, and interference reduction strategies are evaluated. Perceptrons are then applied to two speculative applications: data value prediction and dataflow critical path prediction. Several novel perce ptron-based prediction strategies are proposed for each application that can take advantage of a wider scope of past data in making predictions than previous predictors could. These predictors are evaluated against local table-based approaches on a custom cycle-accurate processor simulator, and are shown on average to have both superior accuracy and higher instruction-per-cycle performance. This work is addressed to computer architects and com puter engineering researchers.

Auteur

earned his Ph.D. in Electrical Engineering at the University of Maryland, College Park. He is currently an Assistant Professor of Computer Science at American University.

Détails sur le produit

Titre: Applying Perceptrons to Speculation in Computer Architecture
Auteur:
Code EAN: 9783639416992
ISBN: 978-3-639-41699-2
Format: Couverture cartonnée
Editeur: AV Akademikerverlag
Genre: Electrotechnique
nombre de pages: 256
Poids: 398g
Taille: H220mm x B150mm x T15mm
Année: 2012
Auflage: Aufl.