Hardware/Software Partitioning for Embedded Systems

Bhuvaneswari, M. C.; Jagadeeswari, M.

doi:10.1007/978-81-322-1958-3_2

Cited by 7 publications

(3 citation statements)

References 25 publications

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“…For multi-objective optimization problems, many evaluation criterions have been defined [24], [26]- [28]. In this paper, S-metric is utilized to evaluate the performance of the algorithms.…”

Section: B Performance Measuresmentioning

confidence: 99%

Using Firework Algorithm for Multi-Objective Hardware/Software Partitioning

et al. 2019

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Hardware/software partitioning plays an important role in the co-design system of software and hardware. It can improve the performance of the embedded system to a great degree. Multi-objective hardware/software partitioning aims to optimize the system performance from multi-aspects simultaneously. In recent years, more and more heuristic algorithms are utilized to solve multi-objective problems. In this paper, we apply a firework algorithm (FWA) to solve the problem of multi-objective hardware/software partitioning. The sorting method for multi-objective solutions is described in detail. The calculation of explosion amplitude is modified according to the number of iterations. Due to binary coding, the method of generating new solutions is updated. Finally, a multi-objective FWA (MOFWA) for multi-objective hardware/software partitioning is proposed. To validate the performance of the MOFWA, experiments on six instances are conducted. The proposed MOFWA is compared with three famous multi-objective optimization algorithms, the nondominated sorting genetic algorithm II, the strength Pareto evolutionary algorithm 2, and the Pareto envelope-based selection algorithm in terms of S-metric. The experimental results show that the MOFWA significantly outperforms the three other algorithms.INDEX TERMS Multi-objective firework algorithm, hardware/software partitioning, multi-objective problem, heuristic algorithm.

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Section: B Performance Measuresmentioning

confidence: 99%

Using Firework Algorithm for Multi-Objective Hardware/Software Partitioning

et al. 2019

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“…Like the GA algorithm, the S A algorithm was used in many studies to deal with the HS P problem, some examples are cited in (Banerjee and Dutt, 2004b,a). GR algorithm starts by a candidate set, and iteratively adds the element that gives the best optimization, the algorithm stops when no improvement is obtained, (Bhuvaneswari and Jagadeeswari, 2015;Lin, 2013) are examples of approaches based on the GR algorithm for the HS P problem. HC algorithm starts with a sub-optimal solution, and repeatedly improves the solution until some conditions are met; unlike the GR algorithm, the HC algorithm has the possibility to avoid the local minima; an example of using the HC algorithm for the HS P problem is given in (Sim et al, 2008).…”

Section: Related Workmentioning

confidence: 99%

“…To overcome this issue, the recent studies focus on heuristic algorithms. Heuristic algorithms give an approximation to the exact solution in a short time, the most classical algorithms are the Genetic Algorithm (Feng et al, 2014), Simulated Annealing algorithm (Banerjee and Dutt, 2004b), Tabu Search algorithm (Lin et al, 2014), Hill Climbing Algorithm (Sim et al, 2008) and Greedy Algorithm (Bhuvaneswari and Jagadeeswari, 2015). The majority of previous works studied the optimization of one metric (cost or performance) while respecting a given constraint on the other metric.…”

Section: Introductionmentioning

confidence: 99%

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

Iguider¹,

Bousselam²,

Elissati³

et al. 2019

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The codesign is a robust methodology, used in modern embedded systems with the objective of achieving the functional specifications and meeting the non-functional requirements. The most interesting step in the codesing is the process of Hardware/Software Partitioning. The aim is to decide which functionalities of the system should be implemented in hardware (HW) or in software (S W). In this article, a new heuristic algorithm is proposed to simultaneously optimize the hardware area (cost) and the execution time (performance) of a multiprocessor system. The proposed algorithm is inspired from game theory and especially from the GO game. The system is modeled using the DAG graph (Data Acyclic Graph), and two players (HW player and SW player) play in turn and choose a block (functionality) from the graph (system). The HW player has the goal of optimizing the global HW area while the SW player has the objective of minimizing the global execution time. After the game termination, and based on the 0-1 Knapsack algorithm, a step of refinement is used to meet the constraint on the total hardware area or on the overall execution time if a constraint is pre-defined. Experimental results show that the proposed algorithm gives better solutions compared to the Simulated Annealing algorithm and the Genetic Algorithm.

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