BDD-Based Synthesis of Reversible Logic

2010 ◽  
Vol 1 (4) ◽  
pp. 25-41 ◽  
Author(s):  
Robert Wille ◽  
Rolf Drechsler

Reversible logic became a promising alternative to traditional circuits because of its applications in emerging technologies such as quantum computing, low-power design, DNA computing, or nanotechnologies. As a result, synthesis of the respective circuits is an intensely studied topic. However, most synthesis methods are limited, because they rely on a truth table representation of the function to be synthesized. In this paper, the authors present a synthesis approach that is based on Binary Decision Diagrams (BDDs). The authors propose a technique to derive reversible or quantum circuits from BDDs by substituting all nodes of the BDD with a cascade of Toffoli or quantum gates, respectively. Boolean functions containing more than a hundred of variables can efficiently be synthesized. More precisely, a circuit can be obtained from a given BDD using an algorithm with linear worst case behavior regarding run-time and space requirements. Furthermore, using the proposed approach, theoretical results known from BDDs can be transferred to reversible circuits. Experiments show better results (with respect to the circuit cost) and a significantly better scalability in comparison to previous synthesis approaches.

Author(s):  
Robert Wille ◽  
Rolf Drechsler

Reversible logic became a promising alternative to traditional circuits because of its applications in emerging technologies such as quantum computing, low-power design, DNA computing, or nanotechnologies. As a result, synthesis of the respective circuits is an intensely studied topic. However, most synthesis methods are limited, because they rely on a truth table representation of the function to be synthesized. In this paper, the authors present a synthesis approach that is based on Binary Decision Diagrams (BDDs). The authors propose a technique to derive reversible or quantum circuits from BDDs by substituting all nodes of the BDD with a cascade of Toffoli or quantum gates, respectively. Boolean functions containing more than a hundred of variables can efficiently be synthesized. More precisely, a circuit can be obtained from a given BDD using an algorithm with linear worst case behavior regarding run-time and space requirements. Furthermore, using the proposed approach, theoretical results known from BDDs can be transferred to reversible circuits. Experiments show better results (with respect to the circuit cost) and a significantly better scalability in comparison to previous synthesis approaches.


2006 ◽  
Vol 45 (4B) ◽  
pp. 3614-3620 ◽  
Author(s):  
Takahiro Tamura ◽  
Isao Tamai ◽  
Seiya Kasai ◽  
Taketomo Sato ◽  
Hideki Hasegawa ◽  
...  

Author(s):  
Nandan Sudarsanam ◽  
Ramya Chandran ◽  
Daniel D. Frey

Abstract This research studies the use of predetermined experimental plans in a live setting with a finite implementation horizon. In this context, we seek to determine the optimal experimental budget in different environments using a Bayesian framework. We derive theoretical results on the optimal allocation of resources to treatments with the objective of minimizing cumulative regret, a metric commonly used in online statistical learning. Our base case studies a setting with two treatments assuming Gaussian priors for the treatment means and noise distributions. We extend our study through analytical and semi-analytical techniques which explore worst-case bounds and the generalization to k treatments. We determine theoretical limits for the experimental budget across all possible scenarios. The optimal level of experimentation that is recommended by this study varies extensively and depends on the experimental environment as well as the number of available units. This highlights the importance of such an approach which incorporates these factors to determine the budget.


2020 ◽  
Vol 20 (10) ◽  
pp. 5977-5996 ◽  
Author(s):  
Saee Gharpure ◽  
Balaprasad Ankamwar

With increase in incidence of multidrug resistant pathogens, there is a demand to adapt newer approaches in order to combat these diseases as traditional therapy is insufficient for their treatment. Use of nanotechnology provides a promising alternative as antimicrobial agents as against traditional antibiotics. Metal oxides have been exploited for a long times for their antimicrobial properties. Zinc oxide nanoparticles (ZnO NPs) are preferred over other metal oxide nanoparticles because of their bio-compatible nature and excellent antibacterial potentials. The basic mechanism of bactericidal nature of ZnO nanoparticles includes physical contact between ZnO nanoparticles and the bacterial cell wall, generation of reactive oxygen species (ROS) as well as free radicals and release of Zn2+ ions. This review focuses on different synthesis methods of ZnO nanoparticles, various analytical techniques frequently used for testing antibacterial properties, mechanism explaining antibacterial nature of ZnO nanoparticles as well as different factors affecting the antibacterial properties.


2013 ◽  
Vol 2013 ◽  
pp. 1-5 ◽  
Author(s):  
S. S. Askar

It is reported in the literature that the most fundamental idea to address uncertainty is to begin by condensing random variables. In this paper, we propose Cournot duopoly game where quantity-setting firms use nonlinear demand function that has no inflection points. A random cost function is introduced in this model. Each firm in the model wants to maximize its expected profit and also wants to minimize its uncertainty by minimizing the cost. To handle this multiobjective optimization problem, the expectation and worst-case approaches are used. A model of two rational firms that are in competition and produce homogenous commodities is introduced using an unknown demand function. The equilibrium points of this model are obtained and their dynamical characteristics such as stability, bifurcation, and chaos are investigated. Complete stability and bifurcation analysis are provided. The obtained theoretical results are verified by numerical simulation.


2016 ◽  
Vol 57 ◽  
pp. 307-343 ◽  
Author(s):  
Nathan R. Sturtevant ◽  
Vadim Bulitko

Real-time agent-centered heuristic search is a well-studied problem where an agent that can only reason locally about the world must travel to a goal location using bounded computation and memory at each step. Many algorithms have been proposed for this problem and theoretical results have also been derived for the worst-case performance with simple examples demonstrating worst-case performance in practice. Lower bounds, however, have not been widely studied. In this paper we study best-case performance more generally and derive theoretical lower bounds for reaching the goal using LRTA*, a canonical example of a real-time agent-centered heuristic search algorithm. The results show that, given some reasonable restrictions on the state space and the heuristic function, the number of steps an LRTA*-like algorithm requires to reach the goal will grow asymptotically faster than the state space, resulting in ``scrubbing'' where the agent repeatedly visits the same state. We then show that while the asymptotic analysis does not hold for more complex real-time search algorithms, experimental results suggest that it is still descriptive of practical performance.


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