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• W3010718100 abstract "Cache memories are an essential component of modern processors and consume a large percentage of their power consumption. Its efficacy depends heavily on the memory demands of the software. Thus, finding the optimal cache for a particular program is not a trivial task and usually involves exhaustive simulation. In this article, we propose a machine learning–based methodology that predicts the optimal cache reconfiguration for any given application, based on its dynamic instructions. Our evaluation shows that our methodology reaches 91.1% accuracy. Moreover, an additional experiment shows that only a small portion of the dynamic instructions (10%) suffices to reach 89.71% accuracy." @default.
• W3010718100 created "2020-03-23" @default.
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• W3010718100 date "2020-03-11" @default.
• W3010718100 modified "2023-09-30" @default.
• W3010718100 title "A Machine Learning Methodology for Cache Memory Design Based on Dynamic Instructions" @default.
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• W3010718100 doi "https://doi.org/10.1145/3376920" @default.
• W3010718100 hasPublicationYear "2020" @default.
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