Cluster Computing

, Volume 22, Supplement 1, pp 335–346 | Cite as

Intelligent computation techniques for optimization of the shortest path in an asynchronous network-on-chip

  • K. IlamathiEmail author
  • P. Rangarajan


Network-on-chip (NoC) offers itself to be a suitable interconnection structure and as a viable alternative for system-on-chip, and hence is employed in very-large-scale integration (VLSI) design. An asynchronous network-on-chip (ANoC) design has low energy consumption because of the absence of the clock. However, obtaining optimal path routing in an asynchronous network-on-chip poses computational complexities. In this work, the shortest optimal paths are determined by employing five contemporary optimization techniques, including the harmony search (HS) algorithm using the Hopfield neural network (HNN) in an ANoC mesh topology. Using optimal parameters, in an ANoC, the energy consumption is significantly reduced and a faster convergence speed is achieved. The HS technique was found to have the least consumption of energy and time-complexity in the investigated techniques. HS technique outperformed all the other techniques in various aspects, making it the most suitable for determining the optimal shortest path.


Asynchronous network-on-chip Shortest path Harmony search algorithm Energy consumption 


  1. 1.
    Shafaghi, S., Shokouhifar, M., Sabbaghi-Nadooshan, R.: Swarm intelligence low power routing in network on chips. Int. J. Energy Inf. Commun. 7(2), 21–40 (2016)Google Scholar
  2. 2.
    Sparso, J., Stensgaard, M.B.: ReNoC: a network-on-chip architecture with reconfigurable topology in networks-on-chip. In: Second ACM/IEEE International Symposium, pp. 55–64. (2008)Google Scholar
  3. 3.
    Martin, A.J., Steininger, A.: Asynchronous techniques for systems-on-chip design. Proc. IEEE 94(6), 1089–1120 (2009)CrossRefGoogle Scholar
  4. 4.
    Karthikeyan, A., Kumar, P.S.: GALS implementation of randomly prioritized buffer-less routing architecture for 3D NoC. Clust. Comput. (2017). Google Scholar
  5. 5.
    Moraes, F., Calazans, N., Mello, A., Moller, L., Ost, L.: Hermes: an infrastructure for low area overhead packet-switching networks on chip. Integr. VLSI J. 38(1), 69–93 (2004)CrossRefGoogle Scholar
  6. 6.
    Lattard, D.E., Beigne, C., Bernard, C., Bour, F., Clermidy, Y., Durand, J., Durupt, D., Varreau, P., Vivet, P., Penard, P., Bouttier, A., Berens, F.: A telecom base band circuit based on an asynchronous network-on-chip. In: Proceedings of the Solid-State Circuits Conference Digest of Technical Papers, pp. 258–601. (2007)Google Scholar
  7. 7.
    Dobkin, R.R., Ginosar, R., Kolodny, A.: QNoC asynchronous router. Integr. VLSI J. 42(2), 103–115 (2009)CrossRefGoogle Scholar
  8. 8.
    Bjerregaard, T., Sparso, J.: Implementation of guaranteed services in the MANGO clockless network-on-chip. Comput. Digital Techniques 153(4), 217–229 (2006)CrossRefGoogle Scholar
  9. 9.
    Geem, Z.W., Lee, K.S., Park, Y.: Application of harmony search to vehicle routing. Am. J. Appl. Sci. 2(12), 1552–1557 (2005)CrossRefGoogle Scholar
  10. 10.
    Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the 6th International Symposium on Micro Machine and Human Science, Japan, pp. 39–43. (1995)Google Scholar
  11. 11.
    Mohemmed, A., Sahoo, N.C.: Efficient computation of shortest paths in networks using particle swarm optimization and noising metaheuristics. Discr. Dyn. Nat. Soc. 2007, 25 (2007)MathSciNetzbMATHGoogle Scholar
  12. 12.
    Dorigo, M.V., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. Part B 26(1), 29–41 (1996)CrossRefGoogle Scholar
  13. 13.
    Srivastava, S., Raperia, H., Badwal, J.: Extended ACO algorithm for path prioritization. Int. J. Comput. Appl. 67(1), 17–21 (2013)Google Scholar
  14. 14.
    Hashim, F.A.: Swarm intelligent application in networks routing problem. Int. J. Comput. Appl. 133(1), 25–28 (2016)Google Scholar
  15. 15.
    Llanes, A., Cecilia, J.M., Sánchez, A., Garcia, J.M., Amos, M., Ujaldon, M.: Dynamic load balancing on heterogeneous clusters for parallel ant colony optimization. Clust. Comput. (2016). Google Scholar
  16. 16.
    Ariyaratne, M.K.A., Pemarathne, W.P.J.: A review of recent advancements of firefly algorithm; a modern nature inspired algorithm. In: Proceedings of the 8th International Research Conference, KDU, pp. 61–66. (2015)Google Scholar
  17. 17.
    Yang, X.-S., He, X.: Firefly algorithm: recent advances and applications. Int. J. Swarm Intell. 1(1), 36–50 (2013)CrossRefGoogle Scholar
  18. 18.
    Yang, X.-S.: Harmony search as a metaheuristic algorithm in music-inspired harmony search algorithm: theory and applications. Stud. Comput. Intell. 191, 1–14 (2009)Google Scholar
  19. 19.
    Wang, J., Zhou, B., Zhou, S.: An improved cuckoo search optimization algorithm for the problem of chaotic systems parameter estimation. Comput. Intell. Neurosci. (2016). Google Scholar
  20. 20.
    Tusiy, S.I., Shawkat, N., Ahmed, M.A., Panday, B., Sakib, N.: Comparative analysis of improved cuckoo search (ICS) algorithm and artificial bee colony (ABC) algorithm on continuous optimization problems. Int. J. Adv. Res. Art. Intell. 4(2), 14–19 (2015)Google Scholar
  21. 21.
    Kumaresan, T., Saravanakumar, S., Balamurugan, R.: Visual and textual features based email spam classification using S-Cuckoo search and hybrid kernel support vector machine. Clust. Comput. (2017). Google Scholar
  22. 22.
    Wang, X.: An introduction to harmony search optimization method. Springer Briefs Comput. Intell. (2015). Google Scholar
  23. 23.
    Abdel-Raouf, O., Metwally, M.A.B.: A survey of harmony search algorithm. Int. J. Comput. Appl. 70(28), 17–26 (2013)Google Scholar
  24. 24.
    Jiang, Z., Zhan, H.: (2015) The application of improved harmony search algorithm for solving shortest path problems. In: International Conference on Computational Science and Engineering, pp. 38–42. Atlantis Press, Amsterdam (2015)Google Scholar
  25. 25.
    He, Z., Pan, B., Liu, Z., Tang, X.: The mechanical arm control based on harmony search genetic algorithm. Clust. Comput. (2017). Google Scholar
  26. 26.
    Rani, K.S.K., Deepa, S.N.: Hybrid evolutionary computing algorithms and statistical methods based optimal fragmentation in smart cloud networks. Clust. Comput. (2017). Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.RMD Engineering CollegeKavaraipettai, ChennaiIndia

Personalised recommendations