Applications suited to approximation often exhibit significant value locality, both in terms of inputs as well as outcomes. In this early stage proposal - the ODIN: Outcome Driven Input Navigated approach to value locality based approximation, we hypothesize that value locality based optimizations for approximate applications should be driven by outcomes i.e., the result of the computation, but navigated with the help of inputs. An outcome-driven approach can enable computation slices, whose outcomes are deemed (approximately) redundant or derivable, to be entirely eliminated resulting in large improvements to execution efficiency. While such an approach provides large potential benefits, we address its design challenges by aiding the outcome-driven approach with input-navigation - attempting to map the value locality characteristics within inputs to that of the outcomes. To enable this, we build a novel taxonomy to categorize value locality and use it to analyze benchmarks from the PERFECT suite. We show that with oracle prediction and an ideal design, more than 80 percent of computations can be eliminated at an SNR of 17.8 or a 90 percent accuracy, thus capable of tremendous performance and energy benefits. Finally, we discuss directions towards achieving optimal benefits.

中文翻译：

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使用 ODIN 基于值局部性的近似

适合近似的应用程序通常在输入和结果方面都表现出重要的价值局部性。在这个早期阶段的提案 - ODIN：基于值局部性的近似值的结果驱动输入导航方法，我们假设近似应用程序的基于值局部性的优化应该由结果驱动，即计算结果，但在输入的帮助下进行导航. 结果驱动的方法可以完全消除其结果被认为（大约）冗余或可推导的计算切片，从而大大提高执行效率。虽然这种方法提供了巨大的潜在好处，我们通过输入导航帮助结果驱动的方法来解决其设计挑战——试图将输入中的价值局部特征映射到结果的局部特征。为了实现这一点，我们构建了一个新颖的分类法来对值位置进行分类，并使用它来分析 PERFECT 套件中的基准。我们表明，通过预言机预测和理想设计，可以在 17.8 的 SNR 或 90% 的准确度下消除 80% 以上的计算，从而能够获得巨大的性能和能源优势。最后，我们讨论了实现最佳收益的方向。在 SNR 为 17.8 或准确度为 90% 的情况下，可以消除 80% 以上的计算，从而获得巨大的性能和能源优势。最后，我们讨论了实现最佳收益的方向。在 SNR 为 17.8 或准确度为 90% 的情况下，可以消除 80% 以上的计算，从而获得巨大的性能和能源优势。最后，我们讨论了实现最佳收益的方向。