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Azadeh Alsadat Emrani Zarandi
Researcher at Shahid Bahonar University of Kerman
Publications - 24
Citations - 295
Azadeh Alsadat Emrani Zarandi is an academic researcher from Shahid Bahonar University of Kerman. The author has contributed to research in topics: Adder & Computer science. The author has an hindex of 8, co-authored 17 publications receiving 223 citations. Previous affiliations of Azadeh Alsadat Emrani Zarandi include Islamic Azad University.
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Journal ArticleDOI
Residue Number Systems: A New Paradigm to Datapath Optimization for Low-Power and High-Performance Digital Signal Processing Applications
TL;DR: The aim in this paper is to show this revolution by discussing interesting development in RNS and foster the innovative use of RNS for more applications by investigating how this unconventional number system can be leveraged to benefit their implementation.
Proceedings ArticleDOI
Research challenges in next-generation residue number system architectures
TL;DR: The carry-free nature of residue number system (RNS) has introduced it as an efficient unconventional number system which has attracted lots of researchers for many decades, but still lots of leakages exist that need to be fully investigated.
Journal ArticleDOI
Reverse Converter Design via Parallel-Prefix Adders: Novel Components, Methodology, and Implementations
Azadeh Alsadat Emrani Zarandi,Amir Sabbagh Molahosseini,Mehdi Hosseinzadeh,Saeid Sorouri,Samuel Antao,Leonel Sousa +5 more
TL;DR: Novel specific hybrid parallel-prefix-based adder components that provide better tradeoff between delay and power consumption are presented to design reverse converters to solve the high power consumption problem.
Journal ArticleDOI
New energy-efficient hybrid wide-operand adder architecture
Fereshteh Jafarzadehpour,Amir Sabbagh Molahosseini,Azadeh Alsadat Emrani Zarandi,Leonel Sousa +3 more
TL;DR: This study presents a new hybrid adder architecture, specifically designed for large operands, based on the premise that in large parallel-prefix adders the least-significant carries are produced much sooner than the most-significant ones.
Journal ArticleDOI
An efficient inexact Full Adder cell design in CNFET technology with high-PSNR for image processing
TL;DR: Simulation results confirmed the superiority of the proposed Full Adder cells compared to others, and the proposed 6TIFA improves PDAPP metric at least 21% and at most 76% compared to its counterparts at 0.9V power supply.