Минимизация частично определенных булевых функций, представленных BDD-графами

Предлагается модификация алгоритма построения минимального BDD-графа, являющегося реализацией частично определенной булевой функции. Алгоритм основан на использовании графа совместимости с последующим выделением клик графа с использованием дополнительных условий. Модификация алгоритма позволяет сократить количество операций при вычислении клик графа без потери точности решения

Minimization of average path length in BDDs by variable reordering

12th International Workshop on Logic and Synthesis, Laguna Beach, California, USA, May 28-30, 2003, pp.207-213.This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. As such, it is in the public domain, and under the provisions of Title 17, United States Code, Section 105, may not be copyrighted.Minimizing the Average Path Length (APL) in a BDD reduces the time needed to evaluate Boolean functions represented by BDDs. This paper describes an efficient heuristic APL minimization procedure based on BDD variable reordering. The reordering algorithm is similar to classical variable sifting with the cost function equal to the APL rather than the number of BDD nodes. The main contribution of our paper is a fast way of updating the APL during the swap of two adjacent variables. Experimental results show that the proposed algorithm effectively minimizing the APL of large MCNC benchmark functions, achieving reductions of up to 47%. For some benchmarks, minimizing APL also reduces the BDD node count

Minimization of lines in reversible circuits

Reversible computing has been theoretically shown to be an efficient approach over conventional computing due to the property of virtually zero power dissipation. A major concern in reversible circuits is the number of circuit lines or qubits which are a limited resource. In this thesis we explore the line reduction problem using a decision diagram based synthesis approach and introduce a line reduction algorithm— Minimization of lines using Ordered Kronecker Functional Decision Diagrams (MOKFDD). The algorithm uses a new sub-circuit for a positive Davio node structure in addition to the existing node structures. We also present a shared node ordering for OKFDDs. OKFDDs are a combination of OBDDs and OFDDs. The experimental results shows that the number of circuit lines and quantum cost can be reduced with our proposed approach.NSER

Automated synthesis and optimization of multilevel logic circuits.

With the increased complexity of Very Large Scaled Integrated (VLSI) circuits, multilevellogic synthesis plays an even more important role due to its flexibility and compactness.The history of symbolic logic and some typical techniques for multilevel logic synthesisare reviewed. These methods include algorithmic approach; Rule-Based approach; BinaryDecision Diagram (BDD) approach; Field Programmable Gate Array(FPGA) approachand several perturbation applications.One new kind of don't cares (DCs), called functional DCs has been proposed for multilevellogic synthesis. The conventional two-level cubes are generalized to multilevel cubes.Then functional DCs are generated based on the properties of containment. The conceptof containment is more general than unateness which leads to the generation of newDCs. A separate C program has been developed to utilize the functional DCs generatedas a Boolean function is decomposed for both single output and multiple output functions.The program can produce better results than script.rugged of SIS, developed by UC Berkeley,both in area and speed in less CPU time for a number of testcases from MCNC andIWLS'93 benchmarks.In certain applications ANDjXOR (Reed-Muller) logic has shown some attractive advantagesover the standard Boolean logic based on AND JOR operations. A bidirectionalconversion algorithm between these two paradigms is presented based on the concept of polarityfor sum-of-products (SOP) Boolean functions, multiple segment and multiple pointerfacilities. Experimental results show that the algorithm is much faster than the previouslypublished programs for any fixed polarity. Based on this algorithm, a new technique calledredundancy-removal is applied to generalize the idea to very large multiple output Booleanfunctions. Results for benchmarks with up to 199 inputs and 99 outputs are presented.Applying the preceding conversion program, any Boolean functions can be expressedby fixed polarity Reed-Muller forms. There are 2n polarities for an n-variable function andthe number of product terms depends on these polarities. The problem of exact polarityminimization is computationally extensive and current programs are only suitable whenn :::; 15. Based on the comparison of the concepts of polarity in the standard Boolean logicand Reed-Muller logic, a fast algorithm is developed and implemented in C language whichcan find the best polarity for multiple output functions. Benchmark examples of up to 25inputs and 29 outputs run on a personal computer are given.After the best polarity for a Boolean function is calculated, this function can be furthersimplified using mixed polarity methods by combining the adjacent product terms. Hence,an efficient program is developed based on decomposition strategy to implement mixedpolarity minimization for both single output and very large multiple output Boolean functions.Experimental results show that the numbers of product terms are much less thanthe results produced by ESPRESSO for some categories of functions

Learning understandable classifier models.

The topic of this dissertation is the automation of the process of extracting understandable patterns and rules from data. An unprecedented amount of data is available to anyone with a computer connected to the Internet. The disciplines of Data Mining and Machine Learning have emerged over the last two decades to face this challenge. This has led to the development of many tools and methods. These tools often produce models that make very accurate predictions about previously unseen data. However, models built by the most accurate methods are usually hard to understand or interpret by humans. In consequence, they deliver only decisions, and are short of any explanations. Hence they do not directly lead to the acquisition of new knowledge. This dissertation contributes to bridging the gap between the accurate opaque models and those less accurate but more transparent for humans. This dissertation first defines the problem of learning from data. It surveys the state-of-the-art methods for supervised learning of both understandable and opaque models from data, as well as unsupervised methods that detect features present in the data. It describes popular methods of rule extraction from unintelligible models which rewrite them into an understandable form. Limitations of rule extraction are described. A novel definition of understandability which ties computational complexity and learning is provided to show that rule extraction is an NP-hard problem. Next, a discussion whether one can expect that even an accurate classifier has learned new knowledge. The survey ends with a presentation of two approaches to building of understandable classifiers. On the one hand, understandable models must be able to accurately describe relations in the data. On the other hand, often a description of the output of a system in terms of its input requires the introduction of intermediate concepts, called features. Therefore it is crucial to develop methods that describe the data with understandable features and are able to use those features to present the relation that describes the data. Novel contributions of this thesis follow the survey. Two families of rule extraction algorithms are considered. First, a method that can work with any opaque classifier is introduced. Artificial training patterns are generated in a mathematically sound way and used to train more accurate understandable models. Subsequently, two novel algorithms that require that the opaque model is a Neural Network are presented. They rely on access to the network\u27s weights and biases to induce rules encoded as Decision Diagrams. Finally, the topic of feature extraction is considered. The impact on imposing non-negativity constraints on the weights of a neural network is considered. It is proved that a three layer network with non-negative weights can shatter any given set of points and experiments are conducted to assess the accuracy and interpretability of such networks. Then, a novel path-following algorithm that finds robust sparse encodings of data is presented. In summary, this dissertation contributes to improved understandability of classifiers in several tangible and original ways. It introduces three distinct aspects of achieving this goal: infusion of additional patterns from the underlying pattern distribution into rule learners, the derivation of decision diagrams from neural networks, and achieving sparse coding with neural networks with non-negative weights

On efficient ordered binary decision diagram minimization heuristics based on two-level logic.

by Chun Gu.Thesis (M.Phil.)--Chinese University of Hong Kong, 1999.Includes bibliographical references (leaves 69-71).Abstract also in Chinese.Chapter 1 --- Introduction --- p.3Chapter 2 --- Definitions --- p.7Chapter 3 --- Some Previous Work on OBDD --- p.13Chapter 3.1 --- The Work of Bryant --- p.13Chapter 3.2 --- Some Variations of the OBDD --- p.14Chapter 3.3 --- Previous Work on Variable Ordering of OBDD --- p.16Chapter 3.3.1 --- The FIH Heuristic --- p.16Chapter 3.3.2 --- The Dynamic Variable Ordering --- p.17Chapter 3.3.3 --- The Interleaving method --- p.19Chapter 4 --- Two Level Logic Function and OBDD --- p.21Chapter 5 --- DSCF Algorithm --- p.25Chapter 6 --- Thin Boolean Function --- p.33Chapter 6.1 --- The Structure and Properties of thin Boolean functions --- p.33Chapter 6.1.1 --- The construction of Thin OBDDs --- p.33Chapter 6.1.2 --- Properties of Thin Boolean Functions --- p.38Chapter 6.1.3 --- Thin Factored Functions --- p.49Chapter 6.2 --- The Revised DSCF Algorithm --- p.52Chapter 6.3 --- Experimental Results --- p.54Chapter 7 --- A Pattern Merging Algorithm --- p.59Chapter 7.1 --- Merging of Patterns --- p.60Chapter 7.2 --- The Algorithm --- p.62Chapter 7.3 --- Experiments and Conclusion --- p.65Chapter 8 --- Conclusions --- p.6