Towards the Exact Minimization of BDDs—An Elitism-Based Distributed Evolutionary Algorithm

Chen, Shan-Tai; Lin, Shaofu; Huang, Li-Te; Wei, Chun-Jen

doi:10.1023/b:heur.0000026899.63797.f9

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…For BDD minimization, in general as well as in approximate settings, using genetic algorithms is a common approach, see, e.g., [10,19,29]. The optimization problem inherently has a multi-dimensional target space, minimizing both the BDD size and the error introduced through the approximation.…”

Section: Greedy Bucket-based Bdd Minimizationmentioning

confidence: 99%

BDD-Based Error Metric Analysis, Computation and Optimization

Keszöcze

2022

IEEE Access

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Approximate Computing is a design paradigm that trades off computational accuracy for gains in non-functional aspects such as reduced area, increased computation speed, or power reduction. Computing the error of the approximated design is an essential step to determine its quality. The computation time for determining the error can become very large, effectively rendering the entire logic approximation procedure infeasible.In this work we extensively analyze various error metrics and approximation operations. We present methods to accelerate the computation of error metric computations by (a) exploiting structural information of the function obtained by applying the analyzed operations and (b) computing estimates of the metrics for multi-output Boolean functions represented as Binary Decision Diagrams (BDDs). We further present a novel greedy, bucket-based BDD minimization framework employing the newly proposed error metric computations to produce Pareto-optimal solutions with respect to BDD size and multiple error metrics. The applicability of the proposed minimization framework is demonstrated by an experimental evaluation. We can report considerable speedups while, at the same time, creating high-quality approximated BDDs. The presented framework is publicly available as open-source software on GitHub.

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Section: Greedy Bucket-based Bdd Minimizationmentioning

confidence: 99%

BDD-Based Error Metric Analysis, Computation and Optimization

Keszöcze

2022

IEEE Access

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“…Binary Decision Diagrams are the dominant boolean function representation in the field of circuit design automation [14]. BDDs have been used in a Genetic Programming context [15,16] as a representation of evolving individuals subject to crossovers and mutations expressed as structural transformations and recently in a machine learning context for compressing probabilistic Prolog programs [17] representing candidate theories. Other interesting uses of BDDs in a logic and constraint programming context are related to representations of finite domains.…”

Section: Related Workmentioning

confidence: 99%

Pairing Functions, Boolean Evaluation and Binary Decision Diagrams in Prolog

Tarau

2008

Preprint

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A "pairing function" J associates a unique natural number z to any two natural numbers x,y such that for two "unpairing functions" K and L, the equalities K(J(x,y))=x, L(J(x,y))=y and J(K(z),L(z))=z hold. Using pairing functions on natural number representations of truth tables, we derive an encoding for Binary Decision Diagrams with the unique property that its boolean evaluation faithfully mimics its structural conversion to a a natural number through recursive application of a matching pairing function. We then use this result to derive ranking and unranking functions for BDDs and reduced BDDs. The paper is organized as a self-contained literate Prolog program, available at http://logic.csci.unt.edu/tarau/research/2008/pBDD.zip.

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A fast BDD Minimization Framework for Approximate Computing

Wendler

Keszöcze

2020

2020 Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE)

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Towards the Exact Minimization of BDDs—An Elitism-Based Distributed Evolutionary Algorithm

Cited by 3 publications

References 23 publications

BDD-Based Error Metric Analysis, Computation and Optimization

BDD-Based Error Metric Analysis, Computation and Optimization

Pairing Functions, Boolean Evaluation and Binary Decision Diagrams in Prolog

A fast BDD Minimization Framework for Approximate Computing

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