A Low-Power Approximate Multiply-Add Unit

Tongxin Yang, Toshinori Sato, Tomoaki Ukezono
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引用次数: 2

Abstract

Modern applications such as image processing and deep learning are error tolerant in some level of inaccuracy. Approximate computing is one of the promising techniques that benefit applications in such domain by trading power for accuracy. This paper supposes applications that prioritize power and area over accuracy and proposes lower-power approximate multiply-add (MAC) unit with reduced area. The proposed MAC unit utilizes an approximate tree compressor (ATC), which was proposed in the previous study. From the experimental designs, it is unveiled that the proposed MAC unit consumes 48.7% less power and its area is 49.7% smaller than the conventional MAC unit.
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一种低功耗近似乘加单元
图像处理和深度学习等现代应用在某种程度上的不准确性是容错的。近似计算是一种很有前途的技术,它以计算能力换取计算精度,从而有利于该领域的应用。本文考虑了功率和面积优先于精度的应用,提出了一种面积较小的低功耗近似乘加(MAC)单元。所提出的MAC单元采用了先前研究中提出的近似树压缩器(ATC)。实验设计表明,与传统的MAC单元相比,本文提出的MAC单元功耗降低48.7%,面积减小49.7%。
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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