Applying Perceptrons to Speculation in Computer Architecture: Neural Networks in Future Microprocessors - Couverture souple

Black, Michael

Couverture souple

ISBN 10 : 3836425963 ISBN 13 : 9783836425964

Editeur : VDM Verlag Dr. Mueller E.K., 2007

Synopsis

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 methods, 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 perceptron- 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 tablebased approaches on a custom cycle-accurate processor simulator, and are shown on average to have both superior accuracy and higher instruction-percycle performance. This work is addressed to computer architects and computer engineering researchers.

A propos du livre

Présentation de l'éditeur

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 me­thods, 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 perce­ptron-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 com­puter engineering researchers.

Biographie de l'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 de l'édition

• Éditeur: VDM Verlag Dr. Mueller E.K.
• Date d'édition: 2007
• Langue: anglais
• ISBN 10: 3836425963
• ISBN 13: 9783836425964
• Reliure: Broché
• Nombre de pages: 256
• Coordonnées du fabricant: non disponible
• Personne responsable: non disponible