Gate-Level Static Approximate Adders: A Comparative Analysis

Author:

Balasubramanian PadmanabhanORCID,Nayar RaunaqORCID,Maskell Douglas L.

Abstract

Approximate or inaccurate addition is found to be viable for practical applications which have an inherent error tolerance. Approximate addition is realized using an approximate adder, and many approximate adder designs have been put forward in the literature targeting an acceptable trade-off between quality of results and savings in design metrics compared to the accurate adder. Approximate adders can be classified into three categories as: (a) suitable for FPGA implementation, (b) suitable for ASIC type implementation, and (c) suitable for FPGA and ASIC type implementations. Among these, approximate adders, which are suitable for FPGA and ASIC type implementations are particularly interesting given their versatility and they are typically designed at the gate level. Depending on the way approximation is built into an approximate adder, approximate adders can be classified into two kinds as static approximate adders and dynamic approximate adders. This paper compares and analyzes static approximate adders which are suitable for both FPGA and ASIC type implementations. We consider many static approximate adders and evaluate their performance for a digital image processing application using standard figures of merit such as peak signal to noise ratio and structural similarity index metric. We provide the error metrics of approximate adders, and the design metrics of accurate and approximate adders corresponding to FPGA and ASIC type implementations. For the FPGA implementation, we considered a Xilinx Artix-7 FPGA, and for an ASIC type implementation, we considered a 32/28 nm CMOS standard digital cell library. While the inferences from this work could serve as a useful reference to determine an optimum static approximate adder for a practical application, in particular, we found approximate adders HOAANED, HERLOA and M-HERLOA to be preferable.

Funder

Ministry of Education

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering

Reference42 articles.

1. https://cloud.google.com/blog/products/ai-machine-learning/an-in-depth-look-at-googles-first-tensor-processing-unit-tpu

2. https://research.google/pubs/pub41694/

3. https://www.tomshardware.com/news/google-tensor-processing-unit-machine-learning,31834.html

4. A k-means clustering algorithm;Hartigan;J. R. Stat. Soc. Ser. C Appl. Stat.,1979

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3