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Memory-Optimized Software Synthesis from Dataflow Program Graphs with Large Size Data Samples

机译:具有大数据样本的数据流程序图的内存优化软件综合

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摘要

In multimedia and graphics applications, data samples of nonprimitive type require significant amount of buffer memory. This paper addresses the problem of minimizing the buffer memory requirement for such applications in embedded software synthesis from graphical dataflow programs based on the synchronous dataflow (SDF) model with the given execution order of nodes. We propose a memory minimization technique that separates global memory buffers from local pointer buffers: the global buffers store live data samples and the local buffers store the pointers to the global buffer entries. The proposed algorithm reduces 67% memory for a JPEG encoder, 40% for an H.263 encoder compared with unshared versions, and 22% compared with the previous sharing algorithm for the H.263 encoder. Through extensive buffer sharing optimization, we believe that automatic software synthesis from dataflow program graphs achieves the comparable code quality with the manually optimized code in terms of memory requirement.
机译:在多媒体和图形应用中,非基本类型的数据样本需要大量的缓冲存储器。本文解决了在基于给定节点执行顺序的情况下,根据基于同步数据流(SDF)模型的图形数据流程序从嵌入式数据综合程序中最小化此类应用程序对此类应用程序的缓冲存储器需求的问题。我们提出了一种内存最小化技术,该技术将全局内存缓冲区与本地指针缓冲区分开:全局缓冲区存储实时数据样本,而本地缓冲区存储指向全局缓冲区条目的指针。与未共享版本相比,所提出的算法将JPEG编码器的内存减少67%,将H.263编码器的内存减少40%,与之前使用H.263编码器的共享算法的内存相比,减少22%。通过广泛的缓冲区共享优化,我们认为从数据流程序图中自动进行软件综合可以在内存需求方面达到与手动优化代码相当的代码质量。

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