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首页> 外文会议>International Conference on Hardware/Software Codesign and System Synthesis >Scalable and realistic benchmark synthesis for efficient NoC performance evaluation: A complex network analysis approach
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Scalable and realistic benchmark synthesis for efficient NoC performance evaluation: A complex network analysis approach

机译：可扩展且切合实际的基准综合以进行有效的NoC性能评估：复杂的网络分析方法

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The complexity of the design-space exploration of large-scale NoCs is exacerbated not only by the ever-increasing number of cores, but also by the increased runtime uncertainties in both the scale and task structure of the emerging applications. Consequently, it is crucial to develop rigorous mathematical frameworks for capturing the task dependencies of varied applications to foster the generation of realistic benchmarks that can guide the NoC design. However, the current NoC benchmark suites either lack portability and poorly scale as they require intensive development efforts on specific architectures and simulation time, or are synthesized based on purely stochastic models that are disconnected with real applications, which may easily lead to biased and/or delayed design choices. To overcome these drawbacks, we propose a benchmark synthesis framework that i) not only allows extraction of dynamical task dependencies of the application and synthesize traffic workloads spatio-temporally consistent with realistic traffic behavior, ii) but can also be easily scaled by the proposed complex-network inspired algorithm for large benchmark generation while preserving key structural features that governs application communication behaviors. We validate the proposed framework on a large-scale simulation environment by running a set of real applications. Experimental results show that the synthesized benchmarks respect the traffic patterns of the original applications and preserve key features of application task structures.
机译：大规模NoC的设计空间探索的复杂性不仅由于核心数量的不断增加，而且由于新兴应用程序的规模和任务结构的运行时不确定性增加而加剧。因此，至关重要的是，开发严格的数学框架来捕获各种应用程序的任务相关性，以培养可指导NoC设计的现实基准。但是，当前的NoC基准套件要么缺乏可移植性，要么由于需要在特定体系结构和仿真时间上进行大量开发工作而扩展性很差，或者是基于与实际应用程序脱节的纯随机模型进行综合的，因此很容易导致偏差和/或延迟的设计选择。为了克服这些缺点，我们提出了一个基准综合框架，该框架不仅可以提取应用程序的动态任务依赖关系，并且可以在时空上与现实的交通行为相一致地综合交通流量，ii），而且可以很容易地通过提议的复杂规模进行扩展。网络启发的算法，用于生成大型基准，同时保留控制应用程序通信行为的关键结构特征。通过运行一组实际应用程序，我们在大规模仿真环境中验证了所提出的框架。实验结果表明，综合基准测试尊重原始应用程序的流量模式，并保留了应用程序任务结构的关键特征。

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