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A novel sign detection method in residue number system based on Chinese remainder theorem with fractional values
•A new sign detection method based on the Chinese Remainder Theorem (CRT) with fractional values implemented using the Wallace tree and the modified Kogge-Stone adder was proposed.•Hardware modelling on FPGA for the proposed method shows that it provides 1.3 36.3 times less hardware costs than the o...
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Published in: | Microprocessors and microsystems 2023-10, Vol.102, p.104940, Article 104940 |
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Main Authors: | , , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites |
Online Access: | Get full text |
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Summary: | •A new sign detection method based on the Chinese Remainder Theorem (CRT) with fractional values implemented using the Wallace tree and the modified Kogge-Stone adder was proposed.•Hardware modelling on FPGA for the proposed method shows that it provides 1.3 36.3 times less hardware costs than the other state-of-the-art (SOTA) methods.•ASIC modelling for the proposed method provides 1.14 35.74 times less hardware costs than the other SOTA methods.•The presented sign detection method can be helpful in RNS-based devices in implementing comparison and division operations.
Sign detection is a non-modular operation in the residue number system (RNS). It requires the calculation of the number positional characteristic represented in the RNS. This work proposes a new sign detection method based on the Chinese Remainder Theorem (CRT) with fractional values implemented using the Wallace tree and the modified Kogge-Stone adder. Hardware modelling on FPGA for the proposed method shows that it provides 1.3 – 36.3 times less hardware costs than the other state-of-the-art (SOTA) methods, and for ASIC modelling the proposed method provides 1.14 – 35.74 times less hardware costs than the other SOTA methods. The presented sign detection method can be helpful in RNS-based devices in implementing comparison and division operations, providing an extension of the RNS application in areas such as cryptography, machine learning, and digital signal processing. |
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ISSN: | 0141-9331 1872-9436 |
DOI: | 10.1016/j.micpro.2023.104940 |