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Synthesis of Finite State Machines - Logic Optimization (Paperback, Softcover reprint of the original 1st ed. 1997)
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Synthesis of Finite State Machines - Logic Optimization (Paperback, Softcover reprint of the original 1st ed. 1997)
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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.
If a cover is not encodable, it is shown how to augment it
minimally until an encodable superset of GPIs is determined. To
handle encodability the authors have extended the frame to satisfy
encoding constraints presented in the second part. The covering
problems generated in the minimization of GPIs tend to be very
large. Recently large covering problems have been attacked
successfully by representing the covering table with binary
decision diagrams (BDD). In the fourth part of the book the authors
introduce such techniques and extend them to the case of the
implicit minimization of GPIs, where the encodability and
augmentation steps are also performed implicitly. Synthesis of
Finite State Machines: Logic Optimization will be of interest to
researchers and professional engineers who work in the area of
computer-aided design of integrated circuits.
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