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Design of novel low power architectures of 4:2, 5:2 compressors and 2-bit counter using 7 nm FinFET technology

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Abstract

The compressor efficiency significantly impacts digital signal processing algorithms and is primarily used in industrial applications. The fin field-effect transistor (FinFET) technology-based circuits are successfully implemented in the industry as a substitute for MOSFETs. This article proposes the new low power-efficient architectures of the 4:2, 5:2 compressors, and 2-bit counter using 7 nm FinFET Technology. The proposed compressors and 2-bit counter are simulated in Cadence tools with an operating frequency of 200 MHz and a supply voltage of 0.7 V with FinFET technology. The proposed 4:2 compressor improves power and power delay product (PDP) improvements of about 78.54 and 12.1\(\%\), and transistor count is reduced to 36 from 50, respectively. The achieved simulated results in FinFET shows that the proposed 5:2 compressors show 78.1\(\%\) improvement in the delay, 63.3\(\%\) improvement in power and 8.12\(\%\) improvement in PDP and transistor count is reduced to 50 from 82 respectively. The improvement of 61.67\(\%\) in power delay product and 74.98\(\%\) is observed in average power, and transistor count is reduced from 20 to 15 in the proposed 2-bit counter design. These proposed designs are compared with the different existing five types of 4:2 compressors and eight types of 5:2 compressors for performance comparison, and proposed novel architectures using FinFET technology designs exhibit significant improvement in PDP.

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NK provided significant contributions to the designs, proposed methods and results. NT makes valuable contributions to the research article literature survey, Drawing the diagrams, explanation of equations,figures and drafted the article. KC suggested the changes in the article.

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Correspondence to Nehru Kandasamy.

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Kandasamy, N., Telagam, N. & Chitra, K. Design of novel low power architectures of 4:2, 5:2 compressors and 2-bit counter using 7 nm FinFET technology. J Ambient Intell Human Comput 14, 2467–2479 (2023). https://doi.org/10.1007/s12652-022-04498-1

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