全系统能量质量缩放的近似硬件技术

2020 International Conference on Electronics, Information, and Communication (ICEIC) Pub Date : 2020-01-01 DOI:10.1109/ICEIC49074.2020.9051208

Younghyun Kim, Joshua San Miguel, Setareh Behroozi, Tianen Chen, Kyuin Lee, Yongwoo Lee, Jingjie Li, Di Wu

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引用次数: 5

摘要

对于错误弹性应用，如机器学习和信号处理，可以通过放松输出质量的准确性约束来显著提高能源效率。本文提出了硬件技术的分类，以利用各种计算机子系统的能源效率和质量之间的权衡。我们根据目标子系统和对动态能量质量缩放的支持对近似硬件技术进行了分类。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Approximate Hardware Techniques for Energy-Quality Scaling Across the System

For error-resilient applications, such as machine learning and signal processing, a significant improvement in energy efficiency can be achieved by relaxing exactness constraint on output quality. This paper presents a taxonomy of hardware techniques to exploit the trade-off between energy efficiency and quality in various computer subsystems. We classify approximate hardware techniques according to target subsystem and support for dynamic energy-quality scaling.

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来源期刊

2020 International Conference on Electronics, Information, and Communication (ICEIC)

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