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https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/2285
Title: | Power-Efficient Mloa For Error Resilient Applications |
Authors: | Guturu, Sahith Uppugunduru, Anil Kumar Thota, Sridhar Ahmed, Syed Ershad |
Keywords: | Approximate Adder Approximate Computing Image Sharpening Low Power |
Issue Date: | 2021 |
Publisher: | 2021 IEEE International Symposium on Smart Electronic Systems (iSES), iSES 2021 |
Citation: | pp. 79-83 |
Abstract: | Approximate Computing is a paradigm shift to meet the future demands of compute-intensive tasks such as media processing, data mining, and recognition. These applications can tolerate errors up to a specific limit. In such applications, addition is one unit that is power-hungry by approximating the adder savings in area, power, and delay can be achieved. This paper presents a technique of approximating the least significant portion in an adder while improvement in accuracy is achieved using OR-based logic. This results in a reduction of area and power without significant compromise in accuracy. Based on the approximation region, we propose three designs with a tradeoff in computation complexity and accuracy. The results prove the efficacy of the proposed designs and an improvement up to 51.39%, improvement in power w.r.t existing designs. © 2021 IEEE.All rights reserved. |
URI: | https://doi.org/10.1109/iSES52644.2021.00029 http://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/2285 |
ISBN: | 9781728187532 9781665416184 |
Appears in Collections: | Conference Papers |
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