Dataflow modeling is well suited for a large variety of applications for modern multi-core architectures, e.g., from the signal processing and the control domain. Furthermore, Design Space Exploration (DSE) can be used to explore mappings of tasks to hardware resources (cores of an MPSoC) and their scheduling to obtain optimized trade-off solutions between throughput and resource costs. However, the throughput evaluation of an implementation candidate via compilation-in-the-loop or simulation-based approaches can be extremely time-consuming. Such a deficiency is very detrimental, because a typical DSE run needs to evaluate thousands of implementation candidates. As a remedy, we propose a hybrid-adaptive DSE where a max-plus algebra-based analytic throughput calculation method is used in the initial DSE phase to enable a fast progress of the search space exploration. However, as this analysis may be inaccurate as neglecting some real-world effects like cache and scheduling overhead, throughput measurements are taken later in the DSE. Moreover, we explore the trade-off between scheduling efficiency of implementation candidates—in favor of reducing concurrency—and exploiting concurrency to a large extent for parallel execution of the application. To find solutions of highest achievable throughput, it is shown that not only highly scheduling efficient implementation candidates but also highly parallel implementation candidates are essential when determining the initial population. In this realm, we contribute a method for diversity-based population initialization. For a representative set of benchmarks, it is shown that the combination of the two major contributions allows us to find much higher throughput multi-core solutions within a given exploration time compared to a state-of-the-art DSE approach.

中文翻译：

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使用结合分析模型和测量的混合评估将数据流应用映射到 MPSoC 的多目标优化

数据流建模非常适合现代多核架构的各种应用，例如来自信号处理和控制领域的应用。此外，设计空间探索 (DSE) 可用于探索任务到硬件资源（MPSoC 的内核）的映射及其调度，以获得吞吐量和资源成本之间的优化权衡解决方案。然而，通过在环编译或基于仿真的方法对实现候选者的吞吐量评估可能非常耗时。这种缺陷是非常有害的，因为典型的 DSE 运行需要评估数千个实现候选者。作为补救措施，我们提出了一个混合自适应 DSE其中，在初始 DSE 阶段使用基于 max-plus 代数的分析吞吐量计算方法，以实现搜索空间探索的快速进展。然而，由于这种分析可能不准确，因为忽略了缓存和调度开销等一些实际影响，因此在 DSE 中稍后会进行吞吐量测量。此外，我们探讨了实现候选者的调度效率（有利于降低并发性）和在很大程度上利用并发性来并行执行应用程序之间的权衡。为了找到可实现的最高吞吐量的解决方案，结果表明，在确定初始种群时，不仅高度调度高效的实现候选者而且高度并行的实现候选者都是必不可少的。在这个境界，我们提供了一种基于多样性的人口初始化方法。对于一组具有代表性的基准，结果表明，与最先进的 DSE 方法相比，这两个主要贡献的结合使我们能够在给定的探索时间内找到更高吞吐量的多核解决方案。