In this paper, we introduce a novel modeling tech- nique to reduce the time associated with cycle-accurate simula- tion 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.

Improving Simulation Speed and Accuracy for Many-Core Embedded Platforms with Ensemble Models

PAONE, EDOARDO;ZACCARIA, VITTORIO;SILVANO, CRISTINA;
2013

Abstract

In this paper, we introduce a novel modeling tech- nique to reduce the time associated with cycle-accurate simula- tion 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.
Design, Automation & Test in Europe Conference & Exhibition (DATE), 2013
9781467350716
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/828939
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