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An Efficient Parallel Reverse Conversion of Residue Code to Mixed-Radix Representation Based on the Chinese Remainder
Mikhail Selianinau1, Yuriy Povstenko1
1Department of Mathematics and Computer Sciences, Faculty of Science and Technology, Jan Dlugosz University in Czestochowa, al. Armii Krajowej 13/15, 42-200 Czestochowa, Poland.
This study introduces a novel parallel method for reverse conversion in Residue Number Systems (RNS). The approach efficiently converts RNS to Mixed-Radix System (MRS) representations with reduced computational complexity.
Area of Science:
- Computer Science
- Number Theory
- Digital Arithmetic
Background:
- Residue arithmetic is crucial for efficient computation.
- Reverse conversion from Residue Number System (RNS) to positional notation is a key non-modular operation.
- Existing methods face challenges in computational efficiency.
Purpose of the Study:
- To present a novel approach for parallel reverse conversion from RNS to Mixed-Radix System (MRS).
- To reduce the computational complexity of RNS to MRS conversion.
- To enhance the efficiency of non-modular operations in residue arithmetic.
Main Methods:
- Developed a parallel reverse conversion algorithm from RNS to MRS.
- Reduced mixed-radix digit calculation to parallel summation of small word-length residues.
- Utilized independent modular channels corresponding to primary RNS moduli.
Main Results:
- Achieved computational complexity of O(k^2)/2 for modular additions and lookup tables, where k is the number of moduli.
- Established a time complexity of O(log2k) modular clock cycles.
- Demonstrated a throughput rate of one reverse conversion per modular clock cycle in pipeline mode.
Conclusions:
- The proposed method offers a significant improvement in parallel reverse conversion efficiency for RNS.
- This novel approach contributes to faster implementation of computational algorithms relying on residue arithmetic.
- The algorithm's parallel nature and reduced complexity make it suitable for high-performance computing applications.
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