MIPAC: Dynamic Input-Aware Accuracy Control for Dynamic Auto-Tuning of Iterative Approximate Computing
MIPAC: Dynamic Input-Aware Accuracy Control for Dynamic Auto-Tuning of Iterative Approximate Computing
复制标题
DOI：
10.1145/3394885.3431551
复制
发表时间：
2021-01
期刊：
影响因子：
--
通讯作者：
Taylor Kemp;Yao Yao-Yao;Younghyun Kim
中科院分区：
文献类型：
--
作者：
Taylor Kemp;Yao Yao-Yao;Younghyun Kim
对于许多具有强大错误恢复能力的应用程序（例如机器学习和信号处理），通过允许中间计算中的轻微错误可以显着提高能源效率和性能。迭代方法（IM）通过多次执行近似算法来改进解决方案，允许通过调整迭代次数（NOI）在运行时进行能量质量权衡。然而，在现有的IM电路中，NOI调整是基于预先表征的NOI质量映射来进行的，其与输入无关，因此导致输出质量的不期望的大变化。在本文中，我们提出了一种新颖的设计框架，其中包含一个轻量级质量控制器，该控制器可以对输出质量进行依赖于输入的预测，并确定运行时的最佳 NOI。所提出的质量控制器由准确且低开销的 NOI 预测器组成，由新颖的逻辑简化技术生成。我们在多个 IM 电路上评估了所提出的设计框架，并展示了能量质量性能的显着改进。
For many applications that exhibit strong error resilience, such as machine learning and signal processing, energy efficiency and performance can be dramatically improved by allowing for slight errors in intermediate computations. Iterative methods (IMs), wherein the solution is improved over multiple executions of an approximation algorithm, allow for energy-quality trade-off at run-time by adjusting the number of iterations (NOI). However, in prior IM circuits, NOI adjustment has been made based on a pre-characterized NOI-quality mapping, which is input-agnostic thus results in an undesirable large variation in output quality. In this paper, we propose a novel design framework that incorporates a lightweight quality controller that makes input-dependent predictions on the output quality and determines the optimal NOI at run-time. The proposed quality controller is composed of accurate yet low-overhead NOI predictors, generated by a novel logic reduction technique. We evaluate the proposed design framework on several IM circuits and demonstrate significant improvements in energy-quality performance.