操作数
乘数(经济学)
对数
计算机科学
算法
聚类分析
架空(工程)
航程(航空)
算术
加法器
人工智能
数学
延迟(音频)
操作系统
电信
数学分析
宏观经济学
复合材料
经济
材料科学
作者
Peipei Yin,Chenghua Wang,Haroon Waris,Weiqiang Liu,Yinhe Han,Fabrizio Lombardi
出处
期刊:IEEE transactions on sustainable computing
[Institute of Electrical and Electronics Engineers]
日期:2021-10-01
卷期号:6 (4): 612-625
被引量:23
标识
DOI:10.1109/tsusc.2020.3004980
摘要
Approximate computing provides an emerging approach to design high performance and low power arithmetic circuits. The logarithmic multiplier (LM) converts multiplication into addition and has inherent approximate characteristics. In this article, dynamic range approximate LMs (DR-ALMs) for machine learning applications are proposed; they use Mitchell’s approximation and a dynamic range operand truncation scheme. The worst case (absolute and relative) errors for the proposed DR-ALMs are analyzed. The accuracy and the hardware overhead of these designs are provided to select the best approximate scheme according to different metrics. The proposed DR-ALMs are compared with the conventional LM with exact operands and previous approximate multipliers; the results show that the power-delay product (PDP) of the best proposed DR-ALM (DR-ALM-6) are decreased by up to 54.07 percent with the mean relative error distance (MRED) decreasing by 21.30 percent compared with 16-bit conventional design. Case studies for three machine learning applications show the viability of the proposed DR-ALMs. Compared with the exact multiplier and its conventional counterpart, the back-propagation classifier with DR-ALMs with a truncation length larger than 4 has a similar classification result for the three datasets; the K-means clustering application with all DR-ALMs has a similar clustering result for four datasets; and the handwritten digit recognition application with DR-ALM-5 or DR-ALM-6 for LeNet-5 achieves similar or even slightly higher recognition rate.
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