In this study, a novel OFF-set based direct-cover Exact minimization Algorithm (EMA) is proposed for single-output Boolean functions represented in a sum-of-products form. To obtain the complete set of prime implicant...
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In this study, a novel OFF-set based direct-cover Exact minimization Algorithm (EMA) is proposed for single-output Boolean functions represented in a sum-of-products form. To obtain the complete set of prime implicants covering the given Target Minterm (ON-minterm), the proposed method uses OFF-cubes (OFF-minterms) expanded by this Target Minterm. The amount of temporary results produced by this method does not exceed the size of the OFF-set. In order to achieve the goal of this study, which is to make faster computations, logic operations were used instead of the standard operations. Expansion OFF-cubes, commutative absorption operations and intersection operations are realized by logic operations for fast computation. The proposed minimization method is tested on several classes of benchmarks and then compared with the ESPRESSO algorithm. The results show that the proposed algorithm obtains more accurate and faster results than ESPRESSO does. (C) 2011 Elsevier Ltd. All rights reserved.
In this paper we present a complete Boolean method for reducing the power consumption in two-level combinational circuits. The two-levellogic optimizer performs the logicminimization for low power targeting static P...
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The paper presents a new approach to solve the unate covering problem based on exploitation of information provided by Lagrangean relaxation. In particular, main advantages of the proposed heuristic algorithm are the ...
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The paper presents a new approach to solve the unate covering problem based on exploitation of information provided by Lagrangean relaxation. In particular, main advantages of the proposed heuristic algorithm are the effective choice of elements to be included in the solution, cost-related reductions of the problem, and a good lower bound on the optimum. The results support the effectiveness of this approach: on a wide set of benchmark problems, the algorithm nearly always hits the optimum and in most cases proves it to be such. On the problems whose optimum is actually unknown, the best known result is strongly improved.
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