PALMINI—fast Boolean minimizer for personal computers
DAC '87 Proceedings of the 24th ACM/IEEE Design Automation Conference
Implicit and incremental computation of primes and essential primes of Boolean functions
DAC '92 Proceedings of the 29th ACM/IEEE Design Automation Conference
Espresso-signature: a new exact minimizer for logic functions
DAC '93 Proceedings of the 30th international Design Automation Conference
Two-level logic minimization: an overview
Integration, the VLSI Journal
Logic Synthesis and Verification Algorithms
Logic Synthesis and Verification Algorithms
Logic Minimization Algorithms for VLSI Synthesis
Logic Minimization Algorithms for VLSI Synthesis
Logic synthesis for vlsi design
Logic synthesis for vlsi design
Proceedings of the 40th annual Design Automation Conference
Synthesis of irregular combinational functions with large don't care sets
Proceedings of the 17th ACM Great Lakes symposium on VLSI
Fuzzy modelling through logic optimization
International Journal of Approximate Reasoning
Dependable design technique for system-on-chip
Journal of Systems Architecture: the EUROMICRO Journal
Column-matching based mixed-mode test pattern generator design technique for BIST
Microprocessors & Microsystems
Efficient method to compute minimum decision chains of Boolean functions
Proceedings of the 21st edition of the great lakes symposium on Great lakes symposium on VLSI
The bacterial strains characterization problem
Proceedings of the 2011 ACM Symposium on Applied Computing
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We present a two-level Boolean minimization tool (BOOM) based on a new implicant generation paradigm. In contrast to all previous minimization methods, where the implicants are generated bottom-up, the proposed method uses a top-down approach. Thus instead of increasing the dimensionality of implicants by omitting literals from their terms, the dimension of a term is gradually decreased by adding new literals. Unlike most other minimization tools like ESPRESSO, BOOM doesn't use the definition of the function to be minimized as a basis for the solution, thus the original coverage influences the solution only indirectly through the number of literals used.Most minimization methods use two basic phases introduced by Quine-McCluskey, known as prime implicant (PI) generation and the covering problem solution. Some more modern methods, like ESPRESSO, combine these two phases, reducing the number of PIs to be processed. This approach is also used in BOOM, the search for new literals to be included into a term aims at maximum coverage of the output function.The function to be minimized is defined by its on-set and off-set, listed in a truth table. Thus the don't care set, often representing the dominant part of the truth table, need not be specified explicitly. The proposed minimization method is efficient above all for functions with a large number of input variables while only few care terms are defined.The minimization procedure is very fast, hence if the first solution does not meet the requirements, it can be improved in an iterative manner. The method has been tested on several different kinds of problems, like the MCNC standard benchmarks or larger problems generated randomly.