An Efficient Majority-Based Compressor For Approximate Computing In The Nano Era
Abstract
Approximate computing is an effective paradigm for energy-efficient hardware design in nanoscale. In this study, an efficient 4:2 compressor for approximate computing in the nano era is proposed. The proposed design includes only two majority gates instead of AND-OR and XOR logics, which leads to circuit efficiency and lower energy consumption. Moreover, the majority operator is the natural logic primitive for several beyond-CMOS technologies such as quantum-dot cellular automata (QCA). The proposed approach is designed using FinFET as a current industrial technology and QCA as a promising emerging nanodevice. FinFETs show lower short channel effects and provide excellent electrostatic characteristics than bulk CMOS for sub-32 nm technologies. Furthermore, QCA will provide extremely high-density and energy-efficient digital circuits for the future VLSI design. In order to evaluate the performance of the proposed approach and make comparisons with the previous designs, extensive simulations are performed using HSPICE, QCADesigner and QCAPro tools. In addition, the proposed compressor is utilized efficiently in image processing applications and the critical metrics in measuring the quality of images are evaluated using MATLAB. The results indicate significant improvements in terms of different performance and accuracy metrics in comparison with the most efficient designs previously presented in the literature.
Publication Date
3-1-2018
Publication Title
Microsystem Technologies
Volume
24
Issue
3
Number of Pages
1589-1601
Document Type
Article
Personal Identifier
scopus
DOI Link
https://doi.org/10.1007/s00542-017-3587-2
Copyright Status
Unknown
Socpus ID
85031780942 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/85031780942
STARS Citation
Moaiyeri, Mohammad Hossein; Sabetzadeh, Farnaz; and Angizi, Shaahin, "An Efficient Majority-Based Compressor For Approximate Computing In The Nano Era" (2018). Scopus Export 2015-2019. 8767.
https://stars.library.ucf.edu/scopus2015/8767