Application-level approximate computing exploits inherent resilience of adaptive applications, and trades off application output quality for runtime system resources. Existing methods treat computing quality as the number of clock cycles to execute a task, but they overlook the fact that the quality of many real-life applications exhibit the characteristic of diminishing return as the processor continues executing. The diminishing return of the quality is largely due to the features of iterative processing or successive refinement inherent in those applications. Ignoring it leads to large over-estimation in contemporary quality optimization approaches. In this article, we exploit the application adaptability to achieve quality maximization by taking both system resource constraints and diminishing return of the quality into account. We first reveal that the diminishing return of the quality is inherent in several well-known applications, and suggest an exponential model that accurately captures it. Second, we propose a dynamic frequency scaling (DFS) methodology to optimally decide the processor execution cycles for such applications, in order to maximize the output quality under system energy, timing, and temperature constraints. We transform the DFS problem to an iterative pseudo quadratic programming heuristic that can be efficiently solved. Third, we present a wrapping dynamic voltage scaling (wDVS) methodology to achieve further quality improvement, by judiciously adjusting the supply voltage to provide extra frequency scaling space. Compared to state-of-the-art algorithms, our approach produces at least 19.1 percent quality improvement on all evaluated cases, with negligible execution overhead.

中文翻译：

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基于DVFS的质量最大化，适用于收益递减的自适应应用

应用程序级近似计算可利用自适应应用程序的固有弹性，并在应用程序输出质量与运行时系统资源之间进行权衡。现有方法将计算质量视为执行任务的时钟周期数，但是它们忽略了以下事实：随着处理器继续执行，许多实际应用程序的质量都呈现出递减收益的特征。质量递减的主要原因是这些应用程序固有的迭代处理或连续改进的功能。忽略它会导致当代质量优化方法中的高估。在本文中，我们通过兼顾系统资源约束和降低质量回报来利用应用程序的适应性来实现质量最大化。首先，我们揭示了质量下降的收益是几种众所周知的应用程序所固有的，并提出了一种能准确捕获质量的指数模型。其次，我们提出一种动态频率缩放（DFS）方法，以最佳地确定此类应用的处理器执行周期，以便在系统能量，时序和温度限制下最大化输出质量。我们将DFS问题转换为可以有效解决的迭代伪二次规划启发式算法。第三，我们通过合理调整电源电压以提供额外的频率缩放空间，提出了一种环绕式动态电压缩放（wDVS）方法，以实现进一步的质量改进。与最新的算法相比，我们的方法对所有评估案例的质量至少提高了19.1％，