KLSAT: An Application Mapping Algorithm Based on Kernighan–Lin Partition and Simulated Annealing for a Specific WK-Recursive NoC Architecture - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2019

KLSAT: An Application Mapping Algorithm Based on Kernighan–Lin Partition and Simulated Annealing for a Specific WK-Recursive NoC Architecture

Feng Shi
  • Fonction : Auteur
  • PersonId : 1161702
Hong Zhang
  • Fonction : Auteur
  • PersonId : 1161703

Résumé

Application mapping is a critical phase in NoC design because of the running time, the network latency and the power consumption. In order to reduce these problems of applications running on multicore architecture, we propose a novel application mapping algorithm, called KLSAT mapping algorithm. It is used for the triplet-based architecture (TriBA) topology which is WK-recursive based networks well conform to a modular design due to the properties of regularity and scalability. The KLSAT mapping algorithm exploits the advantage of both the Kernighan–Lin partitioning algorithm and simulated annealing algorithm to reduce the overall power consumption and network latency. Compared to the random mapping algorithm, the experiment results reveal that the solutions generated by the proposed mapping algorithm reduce average power consumption and network latency by 6.4%, 12.2% in mapping 27 cores and 29.5%, 26.7% in mapping 81 cores respectively.
Fichier principal
Vignette du fichier
486810_1_En_3_Chapter.pdf (541.73 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03770573 , version 1 (06-09-2022)

Licence

Paternité

Identifiants

Citer

Xiaojun Wang, Feng Shi, Hong Zhang. KLSAT: An Application Mapping Algorithm Based on Kernighan–Lin Partition and Simulated Annealing for a Specific WK-Recursive NoC Architecture. 16th IFIP International Conference on Network and Parallel Computing (NPC), Aug 2019, Hohhot, China. pp.31-42, ⟨10.1007/978-3-030-30709-7_3⟩. ⟨hal-03770573⟩
42 Consultations
41 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More