Cooperative Coevolution-based Design Space Exploration for Multi-mode Dataflow Mapping
Cooperative Coevolution-based Design Space Exploration for Multi-mode Dataflow Mapping
复制标题
基于协同进化的多模式数据流映射设计空间探索
DOI：
10.1145/3440246
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发表时间：
2021-03
影响因子：
2
通讯作者：
Xin Yao
中科院分区：
文献类型：
--
作者：
Bo Yuan;Xiaofen Lu;Ke Tang;Xin Yao
一些信号处理和多媒体应用可以由同步低通（SDF）模型来指定。SDF映射到给定的一组异构处理器的问题已经被称为是NP难的，并在设计自动化领域中被广泛研究。然而，现代嵌入式应用程序正变得越来越复杂，随着时间的推移，动态行为的变化。作为对SDF的重要扩展，已经提出了多模式并行流（MMDF）模型来指定具有有限数量的行为（或模式）的这样的应用，并且每个行为（模式）由SDF图表示。MMDF的多处理器映射更具挑战性，因为设计空间随着模式数量的增加而增加。针对传统的基于遗传算法的设计空间探索方法将设计空间作为一个整体进行编码的问题，提出了一种基于协同进化遗传算法的设计空间探索框架，该框架通过一种新的问题分解策略有效地探索设计空间。此外，针对具体问题引入局部搜索算子，作为CCGA全局搜索的补充，进一步提高了整个框架的搜索效率。此外，适应度近似方法和混合适应度评估策略的应用，以减少适应度评估的时间消耗显着。实验结果表明，所提出的DSE方法优于以前的基于遗传算法的方法。所提出的方法可以用更少的优化时间（1/2 - 1/3）获得2×-3倍质量的优化结果。
Some signal processing and multimedia applications can be specified by synchronous dataflow (SDF) models. The problem of SDF mapping to a given set of heterogeneous processors has been known to be NP-hard and widely studied in the design automation field. However, modern embedded applications are becoming increasingly complex with dynamic behaviors changes over time. As a significant extension to the SDF, the multi-mode dataflow (MMDF) model has been proposed to specify such an application with a finite number of behaviors (or modes) and each behavior (mode) is represented by an SDF graph. The multiprocessor mapping of an MMDF is far more challenging as the design space increases with the number of modes. Instead of using traditional genetic algorithm (GA)-based design space exploration (DSE) method that encodes the design space as a whole, this article proposes a novel cooperative co-evolutionary genetic algorithm (CCGA)-based framework to efficiently explore the design space by a new problem-specific decomposition strategy in which the solutions of node mapping for each individual mode are assigned to an individual population. Besides, a problem-specific local search operator is introduced as a supplement to the global search of CCGA for further improving the search efficiency of the whole framework. Furthermore, a fitness approximation method and a hybrid fitness evaluation strategy are applied for reducing the time consumption of fitness evaluation significantly. The experimental studies demonstrate the advantage of the proposed DSE method over the previous GA-based method. The proposed method can obtain an optimization result with 2×−3× better quality using less (1/2−1/3) optimization time.
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DOI：
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发表时间：
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期刊：
ACM Transactions on Embedded Computing Systems (TECS)
影响因子：
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DOI：
10.1109/samos.2011.6045491
发表时间：
2011-07
期刊：
2011 International Conference on Embedded Computer Systems: Architectures, Modeling and Simulation
影响因子：
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