Multiobjective optimization and evolutionary algorithms for the application mapping problem in multiprocessor system-on-chip design
Multiobjective optimization and evolutionary algorithms for the application mapping problem in multiprocessor system-on-chip design
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DOI：
10.1109/tevc.2005.860766
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发表时间：
2006-06
影响因子：
14.3
通讯作者：
Cagkan Erbas;Selin Cerav-Erbas;A. Pimentel
中科院分区：
文献类型：
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作者：
Cagkan Erbas;Selin Cerav-Erbas;A. Pimentel
Sesame是一个软件框架，旨在为异构嵌入式系统的有效设计空间探索开发建模和仿真环境。由于Sesame在单个系统仿真中识别单独的应用程序和架构模型，因此它需要一个显式的映射步骤来将这些模型关联起来进行协同仿真。在映射阶段的设计权衡，即处理时间，功耗和架构成本，被捕获的多目标非线性混合整数规划。本文旨在研究多目标进化算法（MOEAs）在解决大型映射问题上的性能。两个比较的案例研究，它表明，MOEAs提供了一个高度准确的解决方案，在合理的时间设计师。此外，分析了不同的交叉类型，变异的使用，和修复策略的约束处理的目的进行。最后，对多目标优化结果进行了仿真验证。
Sesame is a software framework that aims at developing a modeling and simulation environment for the efficient design space exploration of heterogeneous embedded systems. Since Sesame recognizes separate application and architecture models within a single system simulation, it needs an explicit mapping step to relate these models for cosimulation. The design tradeoffs during the mapping stage, namely, the processing time, power consumption, and architecture cost, are captured by a multiobjective nonlinear mixed integer program. This paper aims at investigating the performance of multiobjective evolutionary algorithms (MOEAs) on solving large instances of the mapping problem. With two comparative case studies, it is shown that MOEAs provide the designer with a highly accurate set of solutions in a reasonable amount of time. Additionally, analyses for different crossover types, mutation usage, and repair strategies for the purpose of constraints handling are carried out. Finally, a number of multiobjective optimization results are simulated for verification.