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Power optimization can reduce heat dissipation costs and has become an important step of circuit logic synthesis. Because the power optimization for mixed polarity Reed–Muller (MPRM) circuits is a combinatorial optimization problem, in this paper, we first propose a multilevel adaptive memetic algorithm (MAMA), which includes global exploration optimizer, local heuristic optimizer, and initial population optimizer. We use the proposed differential evolution optimization, simulated annealing optimization, and data matching algorithm to make the population evolve. Moreover, based on the proposed matrix decomposition strategy and parallel polarity conversion algorithm, we propose a power optimization approach (POA) for MPRM circuits, which searches for an MPRM circuit with a minimum power using the MAMA. Experimental results demonstrated the effectiveness and superiority of the POA in optimizing the power of MPRM circuits. [ABSTRACT FROM AUTHOR]

With the emergence of the multicore architecture and the increase of chip operating frequency, power optimization has become a key step of circuit logic synthesis. Aiming at the XNOR/OR circuits, with the goal of minimizing power, construct the optimal polarity fixed-polarity Reed–Muller (FPRM) circuits power optimization scheme. However, the power optimization for FPRM circuits is a multipeak combinatorial optimization problem, we first propose a metaheuristic optimization algorithm (MOA), which includes the global exploration optimizer, local deep exploitation optimizer, and initial population and uses the proposed differential evolution optimization, fierce wolf siege algorithm-based tabu search, and improved skew tent map to make the population evolve. Based on the proposed Huffman tree construction algorithm and MOA, we propose an efficient power optimization approach (EPOA) to find the minimum power FPRM circuit. Experimental results on the benchmark circuits confirm the effectiveness of EPOA. [ABSTRACT FROM AUTHOR]

At present, area has become one of the main bottlenecks restricting the development of EDA. The area optimization for XNOR/OR-based fixed polarity Reed–Muller (FPRM) circuits aims to find an FPRM circuit with a minimum area. Because the area optimization is a combinatorial optimization problem, we first propose an adaptive bacterial foraging algorithm based on tabu search (ABFA-TS), which includes fuzzy control theory and tabu search strategy. Few studies have considered the problem of polarity conversion sequence. In order to solve the problem of conversion sequence, we propose a hybrid genetic algorithm (HGA) based on the nearest neighbor. Moreover, based on the proposed ABFA-TS and proposed HGA, we propose an area optimization approach for FPRM circuits, which searches for an FPRM circuit with a minimum area. The experimental results confirmed that the maximum time saving rate of HGA reached 78%, and confirmed the superiority of the FPRM area optimization approach in optimizing the FPRM circuits area. • A hybrid genetic algorithm is proposed to solve the switching sequence problem. • An adaptive bacterial foraging algorithm is proposed to solve the combinatorial problem. • An area optimization approach for FPRM logic circuits is proposed. • Experimental results show that the proposed approaches are effective. [ABSTRACT FROM AUTHOR]

Area and power optimization of Fixed Polarity Reed–Muller (FPRM) circuits has received a lot of attention. Polarity optimization for FPRM circuits is essentially a binary multi-objective optimization problem. However, the existing area and power optimization approaches for FPRM logic circuits rarely produce a frontier and a greater number of Pareto optimal solutions. In this paper, a Multi-strategy Multi-objective Artificial Bee Colony (MMABC) algorithm is proposed to solve the binary multi-objective optimization problem. The main innovation of MMABC can be summarized as follows: a flexible foraging behavior strategy for employed bees is proposed to improve the searching ability of the algorithm; a genetic retention evolution for onlooker bees is proposed to improve the quality of the population; an efficient transform strategy is proposed to help the algorithm to jump out the local optimal and increase convergence speed. Moreover, we propose an area and power optimization approach for FPRM logic circuits, which uses the MMABC to search for the polarities (i.e., Pareto optimal solutions) with smaller area and lower power. Experimental results demonstrated the effectiveness and superiority of our approach in optimizing area and power of FPRM logic circuits. [ABSTRACT FROM AUTHOR]

Area and power optimization have become the primary constraints in chip design. Existing area and power optimization approaches have poor optimization performance and high CPU time in logic circuits with multiple inputs. To solve this problems, we propose a multi-strategy bacterial foraging algorithm (MBFA) for multi-objective optimization, which includes nondominated add procedure, dual-population cooperative procedure, high-quality replication procedure, distributed migration procedure and the termination criterion. In addition, considering the characteristics of mixed polarity Reed–Muller (MPRM) logic circuits such as high dimension and large solution space domain, we propose an area and power optimization approach (APOA) based on XNOR/OR, which uses MBFA to search MPRM circuits with Pareto optimal solutions. Experimental results show that the APOA has better performance in optimizing MPRM circuits area and power. • A fast nondominated add procedure is proposed. • A multi-strategy bacterial foraging algorithm for multi-objective optimization is proposed. • An area and power optimization approach for MPRM logic circuits is proposed. [ABSTRACT FROM AUTHOR]