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Fractional rate dataflow model and efficient code synthesis for multimedia applications

机译:用于多媒体应用的分数率DataFlow模型和高效的代码合成

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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:DataFlow程序图中的自动代码合成是一个有希望的高级设计方法,用于多媒体嵌入式系统的快速原型设计。 DataFlow模型的记忆有效的代码综合是一个有源研究,可以在合成代码和手工优化代码之间的内存要求方面降低差距。然而,存在的数据流模型具有有效处理数据结构的固有难度。在本文中,我们提出了一种称为分数率DataFlow(FRDF)的新数据流扩展,其中可以生产和消耗分数样本。在所提出的FRDF模型中,组成数据类型被认为是复合数据类型的一小部分。存在的整数速率数据流模型可以轻松扩展以结合分数率而不会损失分析性质。在本文中,SDF模型扩展到包括FRDF,可以显着降低缓冲存储器需求,对于某些多媒体应用,高达70%。

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