Preface. Part I: From Symbolic to Logic Representations. 1. Introduction. 2. Definitions. 3. Complexity Issues. 4. Encoding for Synthesis. Part II: Constrained Encoding. 5. Symbolic Minimization. 6. Encoding Constraints. Part III: Generalized Prime Implicants. 7. Generalized Prime Implicants. 8. Minimization of GPIS. 9. Encodeability of GPIS. Part IV: Implicit Techniques for Encoding. 10. Implicit Formulation of Unate Covering. 11. Implicit Minimization of GPIS. Part V: Conclusions. 12. Conclusions. References. Index.
Synthesis of Finite State Machines: Logic Optimization is the second in a set of two monographs devoted to the synthesis of Finite State Machines (FSMs). The first volume, Synthesis of Finite State Machines: Functional Optimization, addresses functional optimization, whereas this one addresses logic optimization. The result of functional optimization is a symbolic description of an FSM which represents a sequential function chosen from a collection of permissible candidates. Logic optimization is the body of techniques for converting a symbolic description of an FSM into a hardware implementation. The mapping of a given symbolic representation into a two-valued logic implementation is called state encoding (or state assignment) and it impacts heavily area, speed, testability and power consumption of the realized circuit.
The first part of the book introduces the relevant background, presents results previously scattered in the literature on the computational complexity of encoding problems, and surveys in depth old and new approaches to encoding in logic synthesis.
The second part of the book presents two main results about symbolic minimization; a new procedure to find minimal two-level symbolic covers, under face, dominance and disjunctive constraints, and a unified frame to check encodability of encoding constraints and find codes of minimum length that satisfy them.
The third part of the book introduces generalized prime implicants (GPIs), which are the counterpart, in symbolic minimization of two-level logic, to prime implicants in two-valued two-level minimization. GPIs enable the design of an exact procedure for two-level symbolic minimization, based on a covering step which is complicated by the need to guarantee encodability of the final cover. A new efficient algorithm to verify encodability of a selected cover is presented.
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