Workload-Aware Approximate Computing Configuration
Workload-Aware Approximate Computing Configuration
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DOI：
10.23919/date51398.2021.9474069
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发表时间：
2021-02
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影响因子：
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通讯作者：
Dongning Ma;Rahul Thapa;Xingjian Wang;Xun Jiao;Cong Hao
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作者：
Dongning Ma;Rahul Thapa;Xingjian Wang;Xun Jiao;Cong Hao
由于近似计算在多媒体应用等容错应用中的成功，近年来出现了近似计算。各种近似方法已经证明了放宽特定算术单元的精度要求的有效性。这为探索同时使用多个近似单元来提高效率提供了依据。在本文中，我们的目标是确定程序中近似单元的适当近似配置，以在满足质量约束的情况下最大限度地减少能耗。为此，我们构造了一个约束优化问题，并开发了一个使用遗传算法来求解该问题的工具WOAxC。WOAxC考虑了不同输入工作负载对应用程序质量的影响。我们评估了WOAxC在最小化几个具有不同大小(即操作数量)、工作量(即输入数据集)和质量约束的图像处理应用程序的能量消耗方面的有效性。我们的评估表明，WOAxC提供的配置对于多个近似单元的系统，在质量损失5%、2.5%和0%(无损失)的情况下，平均分别提高了79.6%、77.4%和70.94%的能效。据我们所知，WOAxC是第一个工作负载感知的方法，可以在保证质量的情况下确定适当的近似配置以实现能源最小化。
Approximate computing recently arises due to its success in many error-tolerant applications such as multimedia applications. Various approximation methods have demonstrated the effectiveness of relaxing precision requirements in a specific arithmetic unit. This provides a basis for exploring simultaneous use of multiple approximate units to improve efficiency. In this paper, we aim to identify a proper approximation configuration of approximate units in a program to minimize energy consumption while meeting quality constraints. To do this, we formulate a constrained optimization problem and develop a tool called WOAxC that uses genetic algorithm to solve this problem. WOAxC considers the impact of different input workload on the application quality. We evaluate the efficacy of WOAxC in minimizing the energy consumption of several image processing applications with varying size (i.e., number of operations), workload (i.e., input datasets), and quality constraints. Our evaluation shows that the configuration provided by WOAxC for a system with multiple approximate units improves the energy efficiency by, on average, 79.6%, 77.4%, and 70.94% for quality loss of 5%, 2.5% and 0% (no loss), respectively. To the best of our knowledge, WOAxC is the first workload-aware approach to identify proper approximation configuration for energy minimization under quality guarantee.