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
Type de document :
Communication dans un congrès
MCSoC: Embedded Multicore/Many-core Systems-on-Chip, Sep 2016, Lyon, France. IEEE, 10th IEEE International Symposium on Embedded Multicore/Many-core Systems-on-Chip, 2016, 〈http://mcsoc-forum.org/2016/〉
Liste complète des métadonnées
https://hal-lirmm.ccsd.cnrs.fr/lirmm-01385641
Contributeur : Abdoulaye Gamatié <>
Soumis le : vendredi 21 octobre 2016 - 17:11:40
Dernière modification le : vendredi 19 octobre 2018 - 20:16:01

Identifiants

  • HAL Id : lirmm-01385641, version 1

Collections

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

Citation

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. IEEE, 10th IEEE International Symposium on Embedded Multicore/Many-core Systems-on-Chip, 2016, 〈http://mcsoc-forum.org/2016/〉. 〈lirmm-01385641〉

Exporter

BibTeX TEI DC DCterms EndNote

Partager

Métriques

Consultations de la notice

187

Contact

support.ccsd.cnrs.fr
hal.support@ccsd.cnrs.fr
Logo CCSD