Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Article type: Research Article
Authors: Abiri, Ebrahim* | Bezareh, Zobeideh | Darabi, Abdolreza
Affiliations: Department of Electrical and Electronics Engineering, Shiraz University of Technology, Modarres Boulevard, Shiraz, Iran
Correspondence: [*] Corresponding author. Ebrahim Abiri, Department of Electrical and Electronics Engineering, Shiraz University of Technology, Modarres Boulevard, Shiraz, Iran. Tel.: +98 9123708599; Fax: +98 7137353502; E-mail: [email protected].
Abstract: The memory includes flip flap cells which can store binary information. Therefore by designing the binary storing cell, which has the low chip area and power consumption, the high capacity memories with high performance can be presented. The gate diffusion input (GDI) method employed in designing low power logic gates can decrease the chip area and parasitic and interconnection capacitors. In this paper, the modified-GDI (m-GDI) method is used based on a basic GDI method in order to design master-slave D-Flip Flap (M/S-DFF) circuit. By employing the presented M/S-DFF circuit and the Boolean functions of m-GDI method, Random Access Memory (RAM) cell is designed. For optimizing the performance of the proposed memory cell and gaining the minimum power and propagation delay, the transistors size is adjusted with non-dominated sorting genetic algorithm-II (NSGA-II) in MATLAB software. Finally, the 4-words×4-bits RAM (4×4 RAM) cell is designed and evaluated by employing optimized RAM cell with algorithm. Due to the achieved results, the power consumption of the 4×4 RAM cell is 72.3% , while its propagation delay and Power-Delay Product (PDP) criterion are decreased 75.9% and 93% , respectively, in compare with similar cells which aren’t optimized. The circuit simulation is done with Synopsys H-SPICE simulator in 32 nm CNTFET technology. For ensuring the accurate function of this algorithm, it is experimented on memory cell and decoder. By comparing NSGA-II algorithm with Multiple Objective Particle Swarm Optimization (MOPSO) algorithm, it can be observed that this method has good ability in multi-objective optimization.
Keywords: Gate Diffusion Input (GDI) method, carbon nano tube (CNT) field effect transistor (CNTFET), Non-dominated Sorting Genetic Algorithm II (NSGA- II), Random Access Memory (RAM), Power-Delay Product (PDP), multiple objective particle swarm optimization (MOPSO)
DOI: 10.3233/JIFS-152591
Journal: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 6, pp. 4095-4108, 2017
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
[email protected]
For editorial issues, like the status of your submitted paper or proposals, write to [email protected]
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
[email protected]
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office [email protected]
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
[email protected]
For editorial issues, like the status of your submitted paper or proposals, write to [email protected]
如果您在出版方面需要帮助或有任何建, 件至: [email protected]