Accuracy recovery: A decomposition procedure for the synthesis of partially-specified Boolean functions

Costamagna, Andrea; Micheli, Giovanni De

doi:10.1016/j.vlsi.2022.12.008

Cited by 4 publications

(14 citation statements)

References 9 publications

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“…Synthesis from partial specifications of logical circuits (LSFPS) is reported in [8]. LSFPS is a problem of finding a hardware implementation of partially defined Boolean functions.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…Nevertheless, these methods sacrifice the accuracy of the specifications, which excludes them from legitimate candidates for LSFPS. Paper [8] proposes the restoration of accuracy, which consists in the procedure for comparing the approximate version of the scheme with the new one, which satisfies the exact functionality of the specifications. Experimental testing showed a decrease in the number of gates by 17.38 % and a decrease in the depth of the logic scheme by 12.02 %.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…Methods of simplification of partially defined Boolean functions, which are considered in sources [3][4][5][6][7][8][9], mainly use theoretical objects of related theory, such as Hemming weights, ordered binary decision diagrams (ROBDD), the transformation of a problem from the domain of Boolean algebra into a classical algebraic region, methods for finding approximate Boolean schemes, large training sets of examples with a large number of primary input data, ways to restore the accuracy of a logical scheme, machine learning techniques, as well as software. A mandatory technological point for the implementation of these algorithms and methods is the need for automated calculations.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…The set place of equivalent transformations implicates the algorithm for minimizing Boolean functions. This, in turn, makes it possible to reduce the complexity of the simplification procedure without loss of functionality, compared with algorithms and methods for simplifying partially defined Boolean functions discussed in works [3][4][5][6][7][8][9]. By qualification, the method of figurative transformations belongs to the visual-matrix form of the analytical method [10] and does not exclude the manual technique of minimizing partially defined Boolean functions.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

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Implementing the method of figurative transformations to minimize partially defined Boolean functions

Solomko

Antoniuk

Voitovych

et al. 2023

EEJET

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This paper reports a research that established the possibility of increasing the effectiveness of the method of figurative transformations to minimize partially defined Boolean functions. The method makes it possible, without loss of functionality, to reduce the complexity of the minimization procedure, compared to sorting out binary definitions of partially defined Boolean functions. The interpretation of the result is that the 2-(n, b)-design, 2-(n, x/b)-design systems are a reflection of logical operations. Therefore, the identification of such combinatorial systems in the truth table of logical functions directly and unambiguously establishes the location of logical operations for equivalent transformations of Boolean expressions. This, in turn, implicates an algorithm for simplifying Boolean functions, including partially defined Boolean functions. Thus, the method of figurative transformations simplifies and speeds up the procedure for minimizing partially defined Boolean functions, compared to analogs. This indicates that the visual-matrix form of the analytical method still has the prospect of increasing its hardware capabilities, including in terms of minimizing partially defined Boolean functions. It has been experimentally confirmed that the method of figurative transformations increases the efficiency of minimizing partially defined Boolean functions, compared with analogs, by 100–200 %. There is reason to argue about the possibility of increasing the efficiency of minimizing partially defined Boolean functions in the main and polynomial bases by the specified method. The effectiveness of the method, in particular, is ensured by carrying out all operations of generalized gluing of variables for dead-end disjunctive normal forms (DNF), followed by the use of implicant tables; optimal combination of a sequence of logical operations for gluing variables

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“…Synthesis from partial specifications of logical circuits (LSFPS) is reported in [8]. LSFPS is a problem of finding a hardware implementation of partially defined Boolean functions.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Section: Literature Review and Problem Statementmentioning

confidence: 99%

See 2 more Smart Citations

Implementing the method of figurative transformations to minimize partially defined Boolean functions

Solomko

Antoniuk

Voitovych

et al. 2023

EEJET

View full text Add to dashboard Cite

show abstract

“…However, they are not able to generalize outside of the known variables. In contrast, Costamagn [64] proposes an exact logic synthesis approach called accuracy recovery and improves generalization capabilities. Their method improves approximate logic synthesis approaches by extending the disjoint support decomposition (DSD) technique and considering the existence of the unknown boolean variables.…”

Section: B Logic Learningmentioning

confidence: 99%

Review of Machine Learning in Logic Synthesis

Berndt

Fogaça

Meinhardt

2022

JICS

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Electronic design automation tools have multiple options that need to be tuned for specific designs and technology nodes. Traditionally, the tuning process is done by teams of expert engineers and demands a large amount of computational resources.In recent years, there has been an increased effort to apply machine learning techniques in electronic design automation problems, attempting to increase the design flow correlation and predictability, hence reducing the time spent on tuning.In this work, we revise modern approaches in electronic design automation and machine learning techniques applied during logic synthesis. We categorize and discuss their core technologies, such as transforming data into images. Machine learning techniques are as good as the available data. Thus, we present existing learning datasets for logic synthesis and strategies such as data augmentation to overcome the lack of specific data for logic synthesis problems.To cope with these problems, we discuss how research is shifting from traditional supervised learning techniques to reinforcement learning-based methods.

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Construction of Substitution Boxes Using Finite Fields

Umrani,

Pali,

Dahri

2023

VFAST trans. math.

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Substitution boxes (S-boxes) play a crucial role in modern cryptographic algorithms, providingnon-linear transformations that enhance the security of data encryption. S-boxes are essentialcomponents of symmetric key ciphers, such as Advanced Encryption Standard (AES) and DataEncryption Standard (DES). Non-linearity is a fundamental property that resists linear attacksand enhances the cryptographic strength of S-boxes. Constructing secure and efficient S-boxeswith high non-linearity is a critical challenge in cryptography. The problem of this research isto develop a systematic approach for constructingsubstitution boxes (S-boxes) using finite fields,with a particular focus on achieving optimal non-linearity to enhance the security of cryptographicalgorithms.

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Accuracy recovery: A decomposition procedure for the synthesis of partially-specified Boolean functions

Cited by 4 publications

References 9 publications

Implementing the method of figurative transformations to minimize partially defined Boolean functions

Implementing the method of figurative transformations to minimize partially defined Boolean functions

Review of Machine Learning in Logic Synthesis

Construction of Substitution Boxes Using Finite Fields

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