scholarly journals Binary decision diagram‐based synthesis technique for improved mapping of Boolean functions inside memristive crossbar‐slices

2020 ◽  
Author(s):  
Anindita Chakraborty ◽  
Vivek Maurya ◽  
Sneha Prasad ◽  
Suryansh Gupta ◽  
Rajat Subhra Chakraborty ◽  
...  
Keyword(s):  
Boolean Functions ◽  
Binary Decision Diagram ◽  
Binary Decision ◽  
Synthesis Technique

Binary-decision-diagram-based decomposition of Boolean functions into reversible logic elements

2020 ◽  
Vol 814 ◽  
pp. 120-134 ◽  
Author(s):  
Jia Lee ◽  
Ya-Hui Ye ◽  
Xin Huang ◽  
Rui-Long Yang
Keyword(s):  
Boolean Functions ◽  
Reversible Logic ◽  
Binary Decision Diagram ◽  
Binary Decision ◽  
Decomposition Of Boolean Functions

A Binary Decision Diagram Based Cascade Prognostics Scheme For Power Systems

2020 ◽  
Author(s):  
Ajay Chhokra ◽  
Saqib Hasan ◽  
Abhishek Dubey ◽  
Gabor Karsai
Keyword(s):  
Power Systems ◽  
Binary Decision Diagram ◽  
Binary Decision

Method based on directed graph and binary decision diagram to break logic loops

Kerntechnik ◽  
2019 ◽  
Vol 84 (6) ◽  
pp. 488-499
Author(s):  
S. Yuan ◽  
M. Huifang
Keyword(s):  
Directed Graph ◽  
Binary Decision Diagram ◽  
Binary Decision

Embedded Nanowire Network Growth and Node Device Fabrication for GaAs-Based High-Density Hexagonal Binary Decision Diagram Quantum Circuits

2006 ◽  
Vol 45 (4B) ◽  
pp. 3614-3620 ◽  
Author(s):  
Takahiro Tamura ◽  
Isao Tamai ◽  
Seiya Kasai ◽  
Taketomo Sato ◽  
Hideki Hasegawa ◽  
...  
Keyword(s):  
Binary Decision Diagram ◽  
Device Fabrication ◽  
High Density ◽  
Quantum Circuits ◽  
Network Growth ◽  
Binary Decision ◽  
Nanowire Network

scholarly journals A Novel Graphical Technique for Combinational Logic Representation and Optimization

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-12
Author(s):  
Vedhas Pandit ◽  
Björn Schuller
Keyword(s):  
Boolean Functions ◽  
Discrete Systems ◽  
Boolean Logic ◽  
Decision Diagrams ◽  
New Paradigm ◽  
Binary Decision ◽  
Graphical Technique ◽  
A New Technique ◽  
Logic Functions ◽  
Logical Functions

We present a new technique for defining, analysing, and simplifying digital functions, through hand-calculations, easily demonstrable therefore in the classrooms. It can be extended to represent discrete systems beyond the Boolean logic. The method is graphical in nature and provides complete ‘‘implementation-free” description of the logical functions, similar to binary decision diagrams (BDDs) and Karnaugh-maps (K-maps). Transforming a function into the proposed representations (also the inverse) is a very intuitive process, easy enough that a person can hand-calculate these transformations. The algorithmic nature allows for its computing-based implementations. Because the proposed technique effectively transforms a function into a scatter plot, it is possible to represent multiple functions simultaneously. Usability of the method, therefore, is constrained neither by the number of inputs of the function nor by its outputs in theory. This, being a new paradigm, offers a lot of scope for further research. Here, we put forward a few of the strategies invented so far for using the proposed representation for simplifying the logic functions. Finally, we present extensions of the method: one that extends its applicability to multivalued discrete systems beyond Boolean functions and the other that represents the variants in terms of the coordinate system in use.

scholarly journals An n log n Algorithm for Online BDD Refinement

1995 ◽  
Vol 2 (29) ◽  
Author(s):  
Nils Klarlund
Keyword(s):  
Fixed Points ◽  
Boolean Functions ◽  
Linear Time ◽  
Canonical Representation ◽  
Decision Diagrams ◽  
Total Size ◽  
Binary Decision ◽  
Algebraic Framework ◽  
Verification Systems ◽  
Minimal Fixed Point

Binary Decision Diagrams are in widespread use in verification systems<br />for the canonical representation of Boolean functions. A BDD representing<br />a function phi : B^nu -> N can easily be reduced to its canonical form in<br />linear time.<br />In this paper, we consider a natural online BDD refinement problem<br />and show that it can be solved in O(n log n) if n bounds the size of the<br />BDD and the total size of update operations.<br />We argue that BDDs in an algebraic framework should be understood<br />as minimal fixed points superimposed on maximal fixed points. We propose<br />a technique of controlled growth of equivalence classes to make the<br />minimal fixed point calculations be carried out efficiently. Our algorithm<br />is based on a new understanding of the interplay between the splitting<br />and growing of classes of nodes.<br />We apply our algorithm to show that automata with exponentially<br />large, but implicitly represented alphabets, can be minimized in time<br />O(n log n), where n is the total number of BDD nodes representing the<br />automaton.

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