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.

中文翻译：

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基于协同进化的多模式数据流映射设计空间探索

一些信号处理和多媒体应用程序可以由同步数据流 (SDF) 模型指定。众所周知，SDF 映射到一组给定的异构处理器的问题是 NP 难的，并且在设计自动化领域得到了广泛的研究。然而，随着时间的推移动态行为发生变化，现代嵌入式应用程序变得越来越复杂。作为 SDF 的重要扩展，已提出多模式数据流 (MMDF) 模型来指定具有有限数量的行为（或模式）的应用程序，并且每个行为（模式）由 SDF 图表示。随着设计空间随着模式数量的增加而增加，MMDF 的多处理器映射更具挑战性。代替使用传统的基于遗传算法 (GA) 的设计空间探索 (DSE) 方法将设计空间作为一个整体进行编码，本文提出了一种新的基于协作协同进化遗传算法 (CCGA) 的框架，通过一种新的问题特定分解策略有效地探索设计空间，其中将每个单独模式的节点映射解决方案分配给单独的群体。此外，作为CCGA全局搜索的补充，引入了针对特定问题的局部搜索算子，进一步提高了整个框架的搜索效率。此外，采用适应度逼近方法和混合适应度评估策略，显着减少适应度评估的时间消耗。实验研究证明了所提出的 DSE 方法优于以前的基于 GA 的方法。