Collective Behavior Bees for Solving HW/SW Partitioning and Scheduling Problems in RSoC
Keywords:RSoC, HW/SW partitioning, scheduling, Honey Bee Mating Optimization, Genetic algorithm.
AbstractIn the codesigndomain, many hardware and software techniques must bedeveloped to satisfyspecificconstraints in terms of computation time, area, performance, power consumption, etc.This paperintroduces an automaticapproachTo perform HW/SW partitioning and scheduling such that the global application execution time is minimized and the majority area of FPGA ( Field programmable gate array) used in RSoC ( Reconfigurable System on Chip) is exploited. The usedalgorithmisinspiredby the collective behavior of social insectssuch as bees. : Honey Bees Mating Optimization (HBMO). Comparing the proposed method with Genetic algorithm, the simulation results show that the proposed algorithm has better convergence performance.
(1) Y. Jing, J. Krang,Jiayi DU and B. HU, Application of improved simulated Annealing optimization Algorithms in hardware/Software partitioning of the system on chip,Sringer.pp532-540,2014.
(2) H. Daz Pando, S. Asensi, R. Seplveda et al. An Application of Fuzzy Logic for Hardware/Software Partitioning Embedded Systems. Computation system Vol. 17 No.1, pp. 25-39.2013.
(3) M.B. Abdelhalim, S.E.-D.Habib,An integrated high-levelhardware/software partitioning methodology, Des Autom Embed Syst 15: pp.(19-50), 2011.
(4) P. Arat,., Z.A. Mann, and A. Orbn. Algorithmic aspects of hardware/software partitioning. ACM Transactions on Design Automation of Electronic Systems (TODAES), 10(1), 136156. 2005.
(5) S. Luo, X. Ma, Y. Lu .: An Advanced Non-Dominated Sorting Genetic Algorithm Based SOC Hardware/Software Partitioning, ACTA ELECTRONICA SINICA, (11):pp. 2595-2599. 2009.
(6) Y. Kang, H. Lu, J. He.: A PSO-based Genetic Algorithm for Scheduling of Tasks in a Heterogeneous Distributed System, Journal of software, 8(6): pp.1443-1450. 2013.
(7) F.Vahid Modifying min-cut for hardware and software functional partitioning. 5th International Workshop on Hardware/Software Co- Design (CODES/CASHE97), Braunschweig, Germany, 4348 .1997.
(8) F. Ferrandi, P. C. Lanzi, C. Pilato, D. Sciuto, A. Tumeo. Ant Colony Optimization for Mapping, Scheduling And Placing in Reconfigurable Systems. NASA/ESA Conference on Adaptive Hardware and Systems (AHS),2013.
(9) M. Koudil , K. Benatchba, ATarabet, E. B. Sahraoui. Using bees to solve partitioning and scheduling problem in codesign . Applied mathematics and computation 186,pp1710-1722. 2007.
(10) H. Daz Pando1, S. Cuenca Asensi, R. Seplveda Lima, J. Fajardo Caldern1 and A. Rosete Surez, An Application of Fuzzy Logic for Hardware/Software Partitioning in Embedded Systems. Computacin y Sistemas Vol. 17 No.1, pp. 25-39, 2013.
(11) G. Rehaiem , H. Gharsellaoui, S. Ben Ahmed. A Neural Networks Based Approach for the Real-Time Scheduling of Reconfigurable Embedded Systems with Minimization of Power Consumption. ICIS 2016, Okayama, Japan June 26-29, 2016.
(12) P.Peng, Z. Kuchcinski, K.Doboli, A.. System level hardware/ software partitioning based on simulated annealing and Tabu Search. Design Automation for Embedded Systems, 2(1), 532. 1997.