An efficient memristor crossbar architecture for mapping Boolean functions using Binary Decision Diagrams (BDD)

With the growing intricacy in data engendered and processed across sundry platforms today, the desideratum for consistency has grown. Structured data is utilized for a number of purposes which is not feasible with unstructured data. The purpose of this study was to convert data from unstructured format to structured in portable document format with the help of new framework using the concept of Binary Decision Diagrams and Boolean operations. Binary decision diagrams are data structures for representing Boolean functions taking Boolean as input and generating Boolean as output and hence creating a binary diagram. This research is mainly carried out to show how we can store large number of data easily in the form of bits. The entire focus is on retrieving the meaningful information from unstructured textual data in PDF documents using Boolean operations and bag model, thus, saving the meaningful keywords in the form of binary decision trees. Later on clustering the documents based on commonalities between the documents. This research presents a way for increasing the efficiency of converting unstructured data to structured in PDF and saving huge number of data in the form of bits using this novel framework

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Forward and inverse transformations between Haar spectra and ordered binary decision diagrams of Boolean functions

IEEE Transactions on Computers ◽

10.1109/12.644301 ◽

1997 ◽

Vol 46 (11) ◽

pp. 1272-1279 ◽

Cited By ~ 20

Author(s):

B.J. Falkowski ◽

Chip-Hong Chang

Keyword(s):

Boolean Functions ◽

Binary Decision Diagrams ◽

Decision Diagrams ◽

Ordered Binary Decision Diagrams ◽

Binary Decision

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BDD based construction of resilient functions

Facta universitatis - series Electronics and Energetics ◽

10.2298/fuee1103341s ◽

2011 ◽

Vol 24 (3) ◽

pp. 341-356

Author(s):

Stanislav Stankovic ◽

Jaakko Astola

Keyword(s):

Boolean Functions ◽

Special Class ◽

Binary Decision Diagrams ◽

Compact Representation ◽

Decision Diagrams ◽

Binary Decision ◽

Construction Methods ◽

Resilient Functions ◽

Efficient Construction ◽

High Level

The construction of modern cryptographic systems relies on the so-called resilient Boolean functions, a special class of Boolean functions that possesses a balance between a high level of nonlinearity and correlation immunity. In this paper, we discuss the problem of the compact representation and efficient construction of resilient functions. Binary Decision Diagrams (BDDs) were extensively used as a method of compact representation of various classes of Boolean functions. Furthermore, BDDs offer an opportunity for the efficient implementation of different construction methods for resilient functions. In this paper, we make use of BDDs with attributed edges to provide an implementation of two construction methods proposed by Maitra and Sakar. In addition, we demonstrate that the size of BDDs of resilient functions obtained in this way grows linearly with the number of variables.

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Building free Binary Decision Diagrams using SAT solvers

Facta universitatis - series Electronics and Energetics ◽

10.2298/fuee0703381w ◽

2007 ◽

Vol 20 (3) ◽

pp. 381-394 ◽

Cited By ~ 2

Author(s):

Robert Wille ◽

Görschwin Fey ◽

Rolf Drechsler

Keyword(s):

Boolean Functions ◽

Binary Decision Diagrams ◽

Compact Representation ◽

Decision Diagrams ◽

Ordered Binary Decision Diagrams ◽

Binary Decision ◽

Sat Solvers ◽

Free Binary Decision Diagrams ◽

Sat Solver ◽

Open Question

Free Binary Decision Diagrams (FBDDs) are a data structure for the representation of Boolean functions. In contrast to Ordered Binary Decision Diagrams (OBDDs) FBDDs allow different variable orderings along each path. Thus, FBDDs are the more compact representation while most of the properties of OBDDs are kept. However, how to efficiently build small FBDDs for a given function is still an open question. In this work we propose FBDD construction with the help of SAT solvers. "Recording" the single steps of a SAT solver during the search process leads to an FBDD. Furthermore, by exploiting approaches for identifying isomorphic sub-graphs, i.e. cutlines or cutsets reduced FBDDs are constructed.

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