9783639416992 - Applying Perceptrons to Speculation in Computer Architecture: Neural Networks in Future Microprocessors di Black, Michael (9 risultati)

Condizione: New. pp. 256.

Taschenbuch. Condizione: Neu. Applying Perceptrons to Speculation in Computer Architecture | Neural Networks in Future Microprocessors | Michael Black | Taschenbuch | 256 S. | Englisch | 2012 | AV Akademikerverlag | EAN 9783639416992 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

Paperback. Condizione: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 256 pp. Englisch.

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Black Michaelearned 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.Revision with unchanged content. Modern micro.

Condizione: New. Print on Demand pp. 256.

Condizione: New. PRINT ON DEMAND pp. 256.

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 256 pp. Englisch.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.