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Document Type : Latin Dissertation
Language of Document : English
Record Number : 151195
Doc. No : ET22987
Main Entry : Brian Adam Dazsi
Title Proper : ARTIFICIAL NEURAL NETWORKS FOR BRANCH PREDICTION
Note : This document is digital این مدرک بصورت الکترونیکی می باشد
Abstract : Current microprocessor technology is being enhanced at an amazing rate. Accordingto "Moore's Law", micropmessor transistor count and clock speed doubles every 18months. With the speed that superscalar microprocessors can execute multipleinstructions out-of-order, it is imperzve that an extremely efficient branch predictor isimplemented on the microprocessor, to keep the frequency of recovering Frommispredicted instructions low. With current transistor counts in microp~cessots being sohigh, more complex mi~~~processor components can now be considered. such as multiplepipelines, larger caches, and more complex branch predictors.Artificial Neural Networks have rccently been showing amazing usefulness in theareas of pattern recognition. They have also been used rather well for prediction-....,....-...,..tested for theQ1 PC1 bus cardBoth these projects mere sofixare des elopment efforts tonards contributing to dlfferentaspects of Roboucs and lZ1echatronics projects m the Controls and Roboucs Group..
Subject : Electericl tess
: برق
electronic file name : TL46216.pdf
Title and statement of responsibility and : ARTIFICIAL NEURAL NETWORKS FOR BRANCH PREDICTION [Thesis]
 
 
 
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