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Erschienen in: The Journal of Supercomputing 5/2021

21.10.2020

Novel optimized tree-based stack-type architecture for 2n-bit comparator at nanoscale with energy dissipation analysis

verfasst von: A. Arunkumar Gudivada, Gnanou Florence Sudha

Erschienen in: The Journal of Supercomputing | Ausgabe 5/2021

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Abstract

Comparator is an essential building block in many digital circuits such as biometric authentication, data sorting, and exponents comparison in floating-point architectures among others. Quantum-dot Cellular Automata (QCA) is a latest nanotechnology that overcomes the drawbacks of Complementary Metal Oxide Semiconductor (CMOS) technology. In this paper, novel area optimized 2n-bit comparator architecture is proposed. To achieve the objective, 1-bit stack-type and 4-bit tree-based stack-type (TB-ST) comparators are proposed using QCA. Then, two tree-based architectures of 4-bit comparators are arranged in two layers to optimize the number of quantum cells and area of an 8-bit comparator. Thus, this design can be extended to any 2n-bit comparator. Simulation results of 4-bit and 8-bit comparators using QCADesigner 2.0.3 show that there is a significant improvement in the number of quantum cells and area occupancy. The proposed TB-ST 8-bit comparator uses 2.5 clock cycles and 622 quantum cells with area occupancy of 0.49 µm2 which is an improvement by 10.5% and 38%, respectively, compared to existing designs. Scaling it to a 32-bit comparator, the proposed architecture requires only 2675 quantum cells in an area of 2.05 µm2 with a delay of 3.5 clock cycles, indicating 9.35% and 28.8% improvements, respectively, demonstrating the merit of the proposed architecture. Besides, energy dissipation analysis of the proposed TB-ST 8-bit comparator is simulated on QCADesigner-E tool, indicating average energy dissipation reduction of 17.3% compared to existing works.

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Metadaten
Titel
Novel optimized tree-based stack-type architecture for 2n-bit comparator at nanoscale with energy dissipation analysis
verfasst von
A. Arunkumar Gudivada
Gnanou Florence Sudha
Publikationsdatum
21.10.2020
Verlag
Springer US
Erschienen in
The Journal of Supercomputing / Ausgabe 5/2021
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI
https://doi.org/10.1007/s11227-020-03453-1

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