2012
DOI: 10.1145/2287696.2287702
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An efficient heuristic to identify threshold logic functions

Abstract: CarbondaleA fast method to identify the given Boolean function as a threshold function with weight assignment is introduced. It characterizes the function based on the parameters that have been defined in the literature. The proposed method is capable to quickly characterize all functions that have less than eight inputs and has been shown to operate fast for functions with as many as forty inputs. Furthermore, comparisons with other existing heuristic methods show huge increase in the number of threshold func… Show more

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Cited by 14 publications
(10 citation statements)
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“…Thus, the tool of [7] was modified in order to assign weights of each TLG. The TL function identification method proposed in [20] was employed to check whether a particular Boolean gate cluster can be implemented as a TLG. The fan-in bound of a TLG was set to eight.…”
Section: Resultsmentioning
confidence: 99%
“…Thus, the tool of [7] was modified in order to assign weights of each TLG. The TL function identification method proposed in [20] was employed to check whether a particular Boolean gate cluster can be implemented as a TLG. The fan-in bound of a TLG was set to eight.…”
Section: Resultsmentioning
confidence: 99%
“…By performing reductions in this area, the algorithm proposed typically achieved over 20% fewer clusters, and 40% fewer buffers. Future research will explore using more robust methods for identifying threshold functions such as Gowda et al [2011]; Palaniswamy and Tragoudas [2012], and Palaniswamy et al [2010], in conjunction with balancing paths using the algorithm proposed in this article.…”
Section: Resultsmentioning
confidence: 99%
“…Some examples of TLG nano-implementations are MOBILE and neuMOS [Celinski et al 2000;Beiu et al 2003;Quintana and Avedillo 2008]. This has renewed interest in identification and optimization of TFs Palaniswamy et al 2009Palaniswamy et al , 2010Palaniswamy and Tragoudas 2012a].…”
Section: Introductionmentioning
confidence: 99%
“…The objective of the methods in Palaniswamy et al [2010] and its extended version [Palaniswamy and Tragoudas 2012a] is to determine very quickly the percentage of n input functions that are TFs. The advantage of these methods is that TF identification calculations involve only modified Chow's parameters instead of using linear programming (LP).…”
Section: Introductionmentioning
confidence: 99%
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