In this paper we present a new data structure for representing Boolean functions and an associated set of manipulation algorithms. Functions are represented by directed, acyclic graphs in a manner similar to the representations introduced by Lee [1] and Akers [2], but with further restrictions on the ordering of decision variables in the graph. Although a function requires, in the worst case, a graph of size exponential in the number of arguments, many of the functions encountered in typical applications have a more reasonable representation. Our algorithms have time complexity proportional to the sizes of the graphs being operated on, and hence are quite efficient as long as the graphs do not grow too large. We present experimental results from applying these algorithms to problems in logic design verification that demonstrate the practicality of our approach.

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Graph-Based Algorithms for Boolean Function Manipulation

The Theory of Matrices. By F R. Gantmacher. Two volumes, pp. 374 and 276. 1959. (Translated from the Russian by K. A. Hirsch; Chelsea Publishing Company, New York)

Ordered Binary-Decision Diagrams (OBDDs) represent Boolean functions as directed acyclic graphs. They form a canonical representation, making testing of functional properties such as satisfiability and equivalence straightforward. A number of operations on Boolean functions can be implemented as graph algorithms on OBDD data structures. Using OBDDs, a wide variety of problems can be solved through symbolic analysis. First, the possible variations in system parameters and operating conditions are encoded with Boolean variables. Then the system is evaluated for all variations by a sequence of OBDD operations. Researchers have thus solved a number of problems in digital-system design, finite-state system analysis, artificial intelligence, and mathematical logic. This paper describes the OBDD data structure and surveys a number of applications that have been solved by OBDD-based symbolic analysis.

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Symbolic Boolean manipulation with ordered binary-decision diagrams

SIS is an interactive tool for synthesis and optimization of sequential circuits Given a state transition table, a signal transition graph, or a logic-level description of a sequential circuit, it produces an optimized net-list in the target technology while preserving the sequential input-output behavior Many different programs and algorithms have been integrated into SIS, allowing the user to choose among a variety of techniques at each stage of the process It is built on top of MISII [5] and includes all (combinational) optimization techniques therein as well as many enhancements SIS serves as both a framework within which various algorithms can be tested and compared, and as a tool for automatic synthesis and optimization of sequential circuits This paper provides an overview of SIS The first part contains descriptions of the input specification, STG (state transition graph) manipulation, new logic optimization and verification algorithms, ASTG (asynchronous signal transition graph) manipulation, and synthesis for PGA’s (programmable gate arrays) The second part contains a tutorial example illustrating the design process using SIS

/pdf/sis-a-system-for-sequential-circuit-synthesis-2r3buo7m07.pdf

SIS : A System for Sequential Circuit Synthesis

Sphere Packings, Lattices and Groups

1. Introduction.- 1.1 Design Styles for VLSI Systems.- 1.2 Automatic Logic Synthesis.- 1.3 PLA Implementation.- 1.4 History of Logic Minimization.- 1.5 ESPRESSO-II.- 1.6 Organization of the Book.- 2. Basic Definitions.- 2.1 Operations on Logic Functions.- 2.2 Algebraic Representation of a Logic Function.- 2.3 Cubes and Covers.- 3. Decomposition and Unate Functions.- 3.1 Cofactors and the Shannon Expansion.- 3.2 Merging.- 3.3 Unate Functions.- 3.4 The Choice of the Splitting Variable.- 3.5 Unate Complementation.- 3.6 SIMPLIFY.- 4. The ESPRESSO Minimization Loop and Algorithms.- 4.0 Introduction.- 4.1 Complementation.- 4.2 Tautology.- 4.2.1 Vanilla Recursive Tautology.- 4.2.2 Efficiency Results for Tautology.- 4.2.3 Improving the Efficiency of Tautology.- 4.2.4 Tautology for Multiple-Output Functions.- 4.3 Expand.- 4.3.1 The Blocking Matrix.- 4.3.2 The Covering Matrix.- 4.3.3 Multiple-Output Functions.- 4.3.4 Reduction of the Blocking and Covering Matrices.- 4.3.5 The Raising Set and Maximal Feasible Covering Set.- 4.3.6 The Endgame.- 4.3.7 The Primality of c+.- 4.4 Essential Primes.- 4.5 Irredundant Cover.- 4.6 Reduction.- 4.6.1 The Unate Recursive Paradigm for Reduction.- 4.6.2 Establishing the Recursive Paradigm.- 4.6.3 The Unate Case.- 4.7 Lastgasp.- 4.8 Makesparse.- 4.9 Output Splitting.- 5. Multiple-Valued Minimization.- 6. Experimental Results.- 6.1 Analysis of Raw Data for ESPRESSO-IIAPL.- 6.2 Analysis of Algorithms.- 6.3 Optimality of ESPRESSO-II Results.- 7. Comparisons and Conclusions.- 7.1 Qualitative Evaluation of Algorithms of ESPRESSO-II.- 7.2 Comparison with ESPRESSO-IIC.- 7.3 Comparison of ESPRESSO-II with Other Programs.- 7.4 Other Applications of Logic Minimization.- 7.5 Directions for Future Research.- References.

Logic Minimization Algorithms for VLSI Synthesis

Addressing researchers and graduate students in the active meeting ground of analysis, geometry, and dynamics, this book presents a study of renormalization of quadratic polynomials and a rapid introduction to techniques in complex dynamics. Its central concern is the structure of an infinitely renormalizable quadratic polynomial f(z) = z2 + c. As discovered by Feigenbaum, such a mapping exhibits a repetition of form at infinitely many scales. Drawing on universal estimates in hyperbolic geometry, this work gives an analysis of the limiting forms that can occur and develops a rigidity criterion for the polynomial f. This criterion supports general conjectures about the behavior of rational maps and the structure of the Mandelbrot set.The course of the main argument entails many facets of modern complex dynamics. Included are foundational results in geometric function theory, quasiconformal mappings, and hyperbolic geometry. Most of the tools are discussed in the setting of general polynomials and rational maps.

https://abel.math.harvard.edu/~ctm/papers/home/text/papers/real/book.pdf

Complex Dynamics and Renormalization

In this note we determine the Hausdorff dimension of a family of planar sets which are generalizations of the classical Cantor set.

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The Hausdorff dimension of general Sierpiński carpets

/pdf/mixing-counting-and-equidistribution-in-lie-groups-14zq42do62.pdf

Mixing, counting, and equidistribution in Lie groups

Many parallels between complex dynamics and hyperbolic geometry have emerged in the past decade. Building on work of Sullivan and Thurston, this book gives a unified treatment of the construction of fixed-points for renormalization and the construction of hyperbolic 3- manifolds fibering over the circle.Both subjects are studied via geometric limits and rigidity. This approach shows open hyperbolic manifolds are inflexible, and yields quantitative counterparts to Mostow rigidity. In complex dynamics, it motivates the construction of towers of quadratic-like maps, and leads to a quantitative proof of convergence of renormalization.

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Curtis T. McMullen

Papers

Logic Minimization Algorithms for VLSI Synthesis

Complex Dynamics and Renormalization

The Hausdorff dimension of general Sierpiński carpets

Mixing, counting, and equidistribution in Lie groups

Renormalization and 3-Manifolds Which Fiber over the Circle (AM-142), Volume 142