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Applying Perceptrons to Speculation in Computer Architecture: Neural Networks in Future Microprocessors - Softcover
Synopsis
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.
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About the Author
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.
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Applying Perceptrons to Speculation in Computer Architecture
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AV Akademikerverlag Mai 2012, 2012
ISBN 10: 3639416996
ISBN 13: 9783639416992
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Taschenbuch. Condition: 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. Seller Inventory # 9783639416992
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Applying Perceptrons to Speculation in Computer Architecture
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Condition: 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. Seller Inventory # 4985914
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Applying Perceptrons to Speculation in Computer Architecture: Neural Networks in Future Microprocessors
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Applying Perceptrons to Speculation in Computer Architecture: Neural Networks in Future Microprocessors
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Applying Perceptrons to Speculation in Computer Architecture: Neural Networks in Future Microprocessors
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Applying Perceptrons to Speculation in Computer Architecture
Published by
AV Akademikerverlag Mai 2012, 2012
ISBN 10: 3639416996
ISBN 13: 9783639416992
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Taschenbuch. Condition: 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. Seller Inventory # 9783639416992
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Applying Perceptrons to Speculation in Computer Architecture : Neural Networks in Future Microprocessors
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Taschenbuch. Condition: 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. Seller Inventory # 9783639416992
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Applying Perceptrons to Speculation in Computer Architecture: Neural Networks in Future Microprocessors
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