BOOM-a heuristic Boolean minimizer

Hlavicka, J.; Fišer, Petr

doi:10.1109/iccad.2001.968667

Cited by 32 publications

(29 citation statements)

References 6 publications

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“…In [8] the authors proposed a significant departure from the main design direction by going back to the development of fuzzy models based on the principles of two-valued logic. The wealth of design tools of two-valued logic is immense [4,13,25,43,46], with heuristic techniques producing a simplified, compact and equivalent result in the form of a sumof-products logic description, analogous to the rule-based FNN topology seen in Fig. 2.…”

Section: Structure Discoverymentioning

confidence: 99%

The potential of fuzzy neural networks in the realization of approximate reasoning engines

Gobi

Pedrycz

2006

Fuzzy Sets and Systems

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Section: Structure Discoverymentioning

confidence: 99%

The potential of fuzzy neural networks in the realization of approximate reasoning engines

Gobi

Pedrycz

2006

Fuzzy Sets and Systems

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“…These artificial benchmarks were solved by FC-Min, BOOM [9,10] and ESPRESSO [6] to compare the performance. Here only one iteration of BOOM and FC-Min was performed, the FC-Min depth factor was set to 10:1.…”

Section: Randomly Generated Problems -One Iterationmentioning

confidence: 99%

“…The representatives of these principles are MINI [5], ESPRESSO [6] and its modifications [7], later Scherzo [8] with its improved CP solution algorithm was introduced. Lately we have developed a Boolean minimizer BOOM [9,10], which is able to handle functions with an extremely large number of input variables.…”

Section: Introductionmentioning

confidence: 99%

Boolean minimizer FC-min: coverage finding process

Fišer

Kubátová

2004

Euromicro Symposium on Digital System Design, 2004. DSD 2004.

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This paper describes principles of a novel two-level multi-output Boolean minimizer FC-Min, namely its FindCoverage phase. The problem of Boolean minimization is approached in a reverse way than common minimizers do. First, the cover of the on-set is found, and after that the appropriate implicants are being constructed to satisfy this cover. Thus, only the necessary group implicants are being generated, which makes FC-Min an extremely fast and efficient minimizer for functions with many output variables. An essential phase of the algorithm is the Find Coverage procedure. This phase determines the number of terms in the final solution, which has to be reduced to minimum. It solves an NP-hard problem, thus some heuristic has to be applied. We propose our heuristic method to solve this problem and study the influence of parameters on the final solution quality and runtime.

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“…If the assignment fails, the column-matching process is terminated and the last valid assignment is considered as the final solution. The final valid row assignment forms a truth table, which has to be processed by a minimization tool [6][7][8] to synthesize the final Decoder logic.…”

Section: The Column-matching Algorithmmentioning

confidence: 99%