The Application of Genetic Algorithm in Embedded System Hardware-software Partitioning

Zheng, Shijue; Zhou, Yan; He, Tingting

doi:10.1109/icect.2009.132

Cited by 3 publications

(3 citation statements)

References 6 publications

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“…Performance criticality is the deciding factor for hardware implementation. To achieve this hardware software trade-off many optimization algorithms have been already proposed, such as Tabu search [2,3], simulated annealing [4], Ant colony optimization [5] and Particle swarm optimization [6]. A Genetic Algorithm (GA) [7,8] is a stochastic optimization algorithm modelled on the theory of natural evolution.…”

Section: Introductionmentioning

confidence: 99%

Hardware software partitioning of task graph using genetic algorithm

Mishra

Vakharia

Hati

et al. 2014

International Conference on Recent Advances and Innovations in Engineering (ICRAIE-2014)

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One of the addressable problem in the hardware software co-design is partitioning of functionality on CPU and ASIC/FPGA. The partitioning phase requires the decision for mapping and scheduling of application given as task graphs on a given CPU/ASIC combination. Hardware software partitioning is one of the critical steps to decide which components can be implemented in hardware and which ones implemented in software so that overall system is optimized. Based on a task graph model, this paper presents the optimum solution of problem using genetic algorithm techniques. Experimental results demonstrate that this method can achieve optimized partitioning in terms of cost and delay. A trade off between cost and delay has also been achieved to get the best possible solution.

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Section: Introductionmentioning

confidence: 99%

Hardware software partitioning of task graph using genetic algorithm

Mishra

Vakharia

Hati

et al. 2014

International Conference on Recent Advances and Innovations in Engineering (ICRAIE-2014)

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show abstract

“…Many general-purpose heuristic algorithms are also utilized to solve the system partitioning problem. Simulated annealing-related algorithms [22][23][24], genetic algorithms [8,9,25,26], tabu search, and greedy algorithms [25,27,28] have been extensively used to solve partitioning problem.…”

Section: Introductionmentioning

confidence: 99%

“…Accept the mutation; (24) else (25) Reject the mutation, 1 = 1 ; (26) end if (27) else (28) Accept the mutation; (29) end if / * end of annealing-mutation * / (30) Perform step 19 Based on the definition of previous subsection, hardware cost (x) of the partition (x) and the total time metric (x) can be formalized as follows:…”

mentioning

confidence: 99%

An Effective Heuristic-Based Approach for Partitioning

Zhao

Zhang

Jiang

et al. 2013

Journal of Applied Mathematics

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As being one of the most crucial steps in the design of embedded systems, hardware/software partitioning has received more concern than ever. The performance of a system design will strongly depend on the efficiency of the partitioning. In this paper, we construct a communication graph for embedded system and describe the delay-related constraints and the cost-related objective based on the graph structure. Then, we propose a heuristic based on genetic algorithm and simulated annealing to solve the problem near optimally. We note that the genetic algorithm has a strong global search capability, while the simulated annealing algorithm will fail in a local optimal solution easily. Hence, we can incorporate simulated annealing algorithm in genetic algorithm. The combined algorithm will provide more accurate near-optimal solution with faster speed. Experiment results show that the proposed algorithm produce more accurate partitions than the original genetic algorithm.

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