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Using global code motions to improve the quality of results for high-level synthesis

机译:使用全局代码运动来提高高级综合结果的质量

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The quality of synthesis results for most high-level synthesis approaches is strongly affected by the choice of control flow (through conditions and loops) in the input description. This leads to a need for high-level and compiler transformations that overcome the effects of programming style on the quality of generated circuits. To address this issue, we have developed a set of speculative code-motion transformations that enable movement of operations through, beyond, and into conditionals with the objective of maximizing performance. We have implemented these code transformations, along with supporting code-motion techniques and variable renaming techniques, in a high-level synthesis research framework called Spark. Spark takes a behavioral description in ANSI-C as input and generates synthesizable register-transfer level VHDL. We present results for experiments on designs derived from three real-life multimedia and image processing applications, namely, the MPEG-1 and -2 and GNU image manipulation program applications. We find that the speculative-code motions lead to reductions between 36% and 59% in the number of states in the finite-state machine (controller complexity) and the cycles on the longest path (performance) compared with the case when only nonspeculative code motions are employed. Also, logic synthesis results show fairly constant critical path lengths (clock period) and a marginal increase in area.
机译:对于大多数高级合成方法,合成结果的质量在输入描述中受到控制流(通过条件和循环)的选择的强烈影响。这导致需要高级和编译器转换,以克服编程风格对所生成电路质量的影响。为了解决此问题,我们开发了一组推测性代码移动转换,使操作可以通过,超出和进入条件操作,以实现性能最大化。我们已经在称为Spark的高级综合研究框架中实现了这些代码转换以及支持的代码移动技术和变量重命名技术。 Spark将ANSI-C中的行为描述作为输入,并生成可综合的寄存器传输级别VHDL。我们提供了来自三个现实生活中的多媒体和图像处理应用程序(即MPEG-1和-2以及GNU图像处理程序应用程序)的设计实验结果。我们发现,与仅使用非推测性代码的情况相比,推测性代码的运动导致有限状态机中的状态数(控制器复杂度)和最长路径上的周期(性能)降低了36%至59%。使用动作。而且,逻辑综合结果显示相当恒定的关键路径长度(时钟周期)和面积的少量增加。

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