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Performance Prediction of Application Mapping in Manycore Systems with Artificial Neural Networks

Abdoulaye Gamatié 1 Roman Ursu 1 Manuel Selva 1 Gilles Sassatelli 1
1 SysMIC - Conception et Test de Systèmes MICroélectroniques
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : The growing demand for smarter high-performance embedded systems leads to the integration of multiple functionalities in on-chip systems with tens (even hundreds) of cores. This trend opens a very challenging question about the optimal resource allocation in those manycore systems. Answering this question is key to meet the performance and energy requirements. This paper deals with a learning technique applicable to manycore systems in order to predict mapping-related performances. The resulting prediction models can enable to improve dynamic resource allocation decisions. Our proposal is demonstrated on two automotive application case studies with very promising results.
Keywords : Performance Prediction Manycore Embedded Systems Resource Allocation Artificial Neural Networks
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-01385641
Contributor : Abdoulaye Gamatié <>
Submitted on : Friday, October 21, 2016 - 5:11:40 PM
Last modification on : Monday, May 13, 2019 - 11:22:29 AM

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LIRMM | CNRS | SYSMIC | LIRMM_MIC | MIC | MIPS | UNIV-MONTPELLIER | ADAC

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Abdoulaye Gamatié, Roman Ursu, Manuel Selva, Gilles Sassatelli. Performance Prediction of Application Mapping in Manycore Systems with Artificial Neural Networks. MCSoC: Embedded Multicore/Many-core Systems-on-Chip, Sep 2016, Lyon, France. pp.185-192, ⟨10.1109/MCSoC.2016.17⟩. ⟨lirmm-01385641⟩

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