Hardware/Software Partitioning of Combination of Clustering Algorithm and Genetic Algorithm

Abstract

As to the embedded system of multi-tasks, this

A Modified Binary Firefly Algorithm to Solve Hardware/Software Partitioning Problem

“…For our study, similar to the analysis methods in [47,54,59,65,66,67], the instances of all metrics to be employed in the experiments, are generated randomly. The values of software execution time (Ti Sw ), hardware execution time (Ti Hw ), and hardware area occupied (Ai Hw ) were generated randomly, as in [47,54,59,65,66,67].…”

“…For our study, similar to the analysis methods in [47,54,59,65,66,67], the instances of all metrics to be employed in the experiments, are generated randomly. The values of software execution time (Ti Sw ), hardware execution time (Ti Hw ), and hardware area occupied (Ai Hw ) were generated randomly, as in [47,54,59,65,66,67]. For example, [65] used random values for the two metrics area and hardware execution time generated respectively in [0, 100] and [0, 60] to validate their results.…”

“…53, a hybrid method of PSO and TS was proposed to solve the HW/SW partitioning problems. A combination of the clustering algorithm and the genetic algorithm was developed by Weijia et al [54]. Experiment results have shown that this approach can accelerate converge to an appropriate solution of a complex system with more tasks.…”

GO Game Inspired Algorithm for Hardware Software Partitioning in Multiprocessor Embedded Systems

“…Modern heuristic methods include, inter alia, Genetic Algorithm (GA), Simulated Annealing (S A), Greedy Algorithm (GR), Hill Climbing Algorithm (HC), Tabu Search (T S ) and Particle Swarm Optimization (PS O). GA algorithm is based on the survival of the fitness principle, the main steps are, the initialization of the first population, the parents' selection, performing the crossover to produce the offspring and the mutation of the offspring, the algorithm iterates from the selection step and evaluate each newly generated population, the algorithm terminates when the best individual that meet the termination condition is found; different approaches based on GA algorithm were proposed to solve the HS P problem, some of those approaches are given in (Feng et al, 2014;Zhao et al, 2013;Purnaprajna et al, 2007;Knerr et al, 2007;Li et al, 2014). S A algorithm consists of an analogy between the combinatorial optimization problem and the solid annealing process; the algorithm starts with an initial solution S and a parameter T (temperature), and at each iteration the algorithm generates some neighbors of the current solution, and probabilistically decides between keeping the current solution or replacing it by the best neighbor, and gradually decreases the temperature T ; the algorithm iterates until a good enough solution is found for the system.…”