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A complexity effective communication model for behavioral modeling of signal processing applications

机译:用于信号处理应用程序行为建模的复杂有效通信模型

摘要

In this paper, we argue that the address space of memory regions that participate in inter task communication is over-specified by the traditional communication models used in behavioral modeling, resulting in sub-optimal implementations. We propose shared messaging communication model and the associated channels for efficient inter task communication of high bandwidth data streams in behavioral models of signal processing applications. In shared messaging model, tasks communicate data through special memory regions whose address space is unspecified by the model without introducing non determinism. Address space to these regions can be assigned during mapping of application to specific architecture, by exploring feasible alternatives. We present experimental results to show that this flexibility reduces the complexity (e.g., communication latency, memory usage) of implementations significantly (up to an order of magnitude).
机译:在本文中,我们认为参与任务间通信的内存区域的地址空间被行为建模中使用的传统通信模型过度指定,导致实现次优。我们提出了共享的消息传递通信模型和相关的通道,以在信号处理应用程序的行为模型中实现高带宽数据流的高效任务间通信。在共享消息传递模型中,任务通过其模型未指定地址空间的特殊内存区域传递数据,而不会引入不确定性。通过探索可行的替代方案,可以在将应用程序映射到特定体系结构期间为这些区域分配地址空间。我们提供的实验结果表明,这种灵活性可以显着(多达一个数量级)降低实现的复杂性(例如,通信延迟,内存使用)。

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