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Fractional rate dataflow model and efficient code synthesis for multimedia applications

机译:分数速率数据流模型和多媒体应用程序的高效代码合成

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Automatic code synthesis from dataflow program graphs is a promising high-level design methodology for rapid prototyping of multimedia embedded systems. Memory efficient code synthesis from dataflow models has been an active research subject to reduce the gap in terms of memory requirements between the synthesized code and the hand-optimized code. However, existent dataflow models have inherent difficulty of efficiently handling data structures. In this paper, we propose a new dataflow extension called fractional rate dataflow (FRDF) in which fractional number of samples can be produced and consumed. In the proposed FRDF model, a constituent data type is considered as a fraction of the composite data type. Existent integer rate dataflow models can be easily extended to incorporate the fractional rates without loosing analytical properties. In this paper, the SDF model is extended to include FRDF, which can reduce the buffer memory requirements significantly, up to 70%, for some multimedia applications.
机译:从数据流程序图中自动进行代码合成是用于多媒体嵌入式系统快速原型开发的有前途的高级设计方法。数据流模型中的内存有效代码合成一直是一个积极的研究课题,目的是减少合成代码和手动优化代码之间在内存需求方面的差距。但是,现有的数据流模型具有有效处理数据结构的固有困难。在本文中,我们提出了一种新的数据流扩展,称为分数速率数据流(FRDF),可以在其中生成和使用分数的样本。在建议的FRDF模型中,构成数据类型被认为是复合数据类型的一部分。可以轻松扩展现有的整数速率数据流模型,以合并分数速率,而不会丢失分析属性。在本文中,将SDF模型扩展为包括FRDF,对于某些多媒体应用程序,它可以显着减少缓冲存储器的需求,最高可达70%。

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