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• W2016883306 abstract "In this paper, we introduce a novel modeling technique to reduce the time associated with cycle-accurate simulation of parallel applications deployed on many-core embedded platforms. We introduce an ensemble model based on artificial neural networks that exploits (in the training phase) multiple levels of simulation abstraction, from cycle-accurate to cycle-approximate, to predict the cycle-accurate results for unknown application configurations. We show that high-level modeling can be used to significantly reduce the number of low-level model evaluations provided that a suitable artificial neural network is used to aggregate the results. We propose a methodology for the design and optimization of such an ensemble model and we assess the proposed approach for an industrial simulation framework based on STMicroelectronics STHORM (P2012) many-core computing fabric." @default.
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• W2016883306 date "2013-03-18" @default.
• W2016883306 modified "2023-09-23" @default.
• W2016883306 title "Improving simulation speed and accuracy for many-core embedded platforms with ensemble models" @default.
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• W2016883306 doi "https://doi.org/10.5555/2485288.2485452" @default.
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