Performance Evaluation of Quine-McCluskey Method on Multi-core CPU

Vu, Hoang-Gia; Bui, Ngoc-Dai; Nguyen, Anh‐Tu; ThanhBangLe,

doi:10.1109/nics54270.2021.9701506

Cited by 5 publications

(4 citation statements)

References 9 publications

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“…Although the Quine McCluskey approach is amenable to automation as a computer program, it is inefficient in terms of execution time and memory usage, so that adding one additional literal will practically quadruple these two consequences of the reduction cost [23]. In conclusion, the Quine McCluskey method is far from understandable and visually appealing [24,17], yet it is effective for a small number of input literals and output functions. Khedr, Ramadan, and Abdel-Magid [25] proposed a new algorithm Quine-McCluskey Rule (QMR) for association rule minimization in the dataset depending on Quine-McCluskey (Q-M) algorithm Logic circuits Optimization Technique.…”

Section: Introductionmentioning

confidence: 94%

“…The algorithms in this approach do not achieve distinguished success because they inherit the drawbacks of the methods of the logic design or because they suffer from weak points resulting from the incomplete matching between the FI mining, FIM, problem and the logic circuit design. So let's consider the three most famous algorithms for logic circuit design, which are the Karnaugh map or Kmap [16], Quine-McCluskey Q-M [17], and Espresso [18], with the three algorithms that emerged from them to mine association rules, with a brief description of the drawbacks inhered from the former algorithms:…”

Section: Introductionmentioning

confidence: 99%

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A Logic Design-based Approach for Frequent Itemsets Mining Using LCO Algorithm

2023

IJIES

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Frequent itemsets (FIs) mining is the main challenge in analyzing association rules since the complexity of association rule mining is determined mainly by identifying all frequent itemsets. There are many approaches to FIs mining; each approach includes many algorithms, but there is a major weakness in the logic-design-based FIs approach because there is a limited number of methods for logic circuit or expression optimization, each of which suffers from some drawbacks that are transferred to the mining process when they are utilized for this purpose. We propose LCOFI, a new algorithm for frequent itemset mining based on the LCO algorithm, in this paper. LCOA is a new algorithm for logic circuit optimization. LCOFI utilizes the suggested operations for a bipartite graph in LCOA. The proposed algorithm is simple and efficient and supports a large number of input items. It scans the transaction database only once to construct the bipartite graphs of frequent 1-itemsets and avoids the generation of candidate itemsets. It does not require complex data structures such as trees and hash tables to validate the frequency of FIs. When applied to a variety of datasets with varying characteristics, the proposed algorithm, LCOFI, outperformed the Apriori and Fp-growth algorithms in all the experiments.

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Section: Introductionmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

A Logic Design-based Approach for Frequent Itemsets Mining Using LCO Algorithm

2023

IJIES

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“…Existing exact methods can be computationally expensive for large and highly variable functions [10][11][12], making them impractical for use in some applications, therefore approximate and heuristic algorithms have been proposed to tackle the problem of prime implicants computation scalability. Heuristic approaches including genetic algorithms, simulated annealing, and Tabu search involve randomly generating candidate solutions and iteratively refining them based on fitness functions [13][14][15][16][17].…”

Section: Original Researchmentioning

confidence: 99%

Recursive Ternary-Based Algorithm for Computing Prime Implicants of Multi-Output Boolean Functions

Al-Wardi

2023

J. eng. sustain. dev.

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The problem of computing the set of prime implicants to represent a Boolean function is a classical problem that is still considered a running problem for research because all known approaches have limitations. The article reviews existing methods for computing prime implicants and highlights their limitations, particularly for multi-output functions and limited scalability due to the growth in memory required to complete the computation. Then it proposes a recursive ternary-based minimization algorithm to compute the prime implicants of multi-output Boolean functions. The algorithm is based on the concept of Programmable Logic Array (PLA) tables, which provide a structured and efficient representation of Boolean functions. The algorithm takes advantage of the ternary logic system to efficiently compute the prime implicants while maintaining scalability for large and complex functions, which has significant implications for digital circuit design and optimization.

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“…Quine McCluskey algorithm lends itself to be automated as a computer program, but it is inefficient in terms of execution time and memory consumption such that adding an extra literal will almost double these two ramifications of the minimization cost [8]. In a conclusion, the Quine McCluskey algorithm is efficient for a restricted number of input literals and output functions in addition to its farness from understandability and visualization [9], [10]. Brayton et al [11] developed what so-called ESPRESSO algorithm that keeps a very accepted level of computer resources usage and performance efficiency.…”

Section: Introductionmentioning

confidence: 96%

A new logic circuits optimization algorithm using bipartite graph

Al-Ghanimi

Khafaji²

2022

IJEECS

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Designing a logic circuit from the scratch requires its description in logical expression, (e.g. sum of products), and then the expression should be optimized to diminish the cost and complexity of the circuit by reducing the number of literals, the number of logical terms, and/or logical operations. Karnaugh map, K-Map, is the most popular method in the optimization process, but it suffers from many drawbacks such as its inefficiency or the inability to be used in minimizing logical expression containing more than four literals, in addition to the complexity of implementing it as a program. In this paper, we propose a new algorithm to optimize the logic circuits depending on the bipartite graph and some of the suggested mathematical operations. The proposed algorithm is simple for programming implementation, literal-unlimited number, and is easy to be visualized and understandable. Many of the logic circuits of 3, 4, 5, and 6 literals were optimized and the results were correctly matched with the results of the Karnaugh map. Also, tens of logic circuits of more than 6 literals are optimized and the results were correctly checked with their truth tables and Logic-Friday tool.

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Performance Evaluation of Quine-McCluskey Method on Multi-core CPU

Cited by 5 publications

References 9 publications

A Logic Design-based Approach for Frequent Itemsets Mining Using LCO Algorithm

A Logic Design-based Approach for Frequent Itemsets Mining Using LCO Algorithm

Recursive Ternary-Based Algorithm for Computing Prime Implicants of Multi-Output Boolean Functions

A new logic circuits optimization algorithm using bipartite graph

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