Abstract: Area and power need to be considered at all stages of integrated circuit design. Area and power cooperative optimization for mixed polarity Reed‒Muller (MPRM) circuits aims to find the Pareto non-dominated sets with minimum area and power. Existing multiobjective optimization approaches for MPRM circuits have a long runtime and cannot handle large-scale circuits. In this paper, we propose a multi-strategy quantum wolf pack algorithm (MQWPA), which includes quantum encoding, dual-population strategy, dynamic reverse learning strategy, mutation strategy, and termination criterion. Moreover, based on the proposed MQWPA, we propose an efficient area and power optimization approach (EAPOA), which searches for XNOR/OR-based MPRM circuits with Pareto non-dominated solution sets. Experimental results show that compared with state-of-the-art evolutionary algorithms, MQWPA can search for optimal solutions in multiobjective test problems; compared with commonly used Boolean minimization methods, EAPOA has advantages in area and power consumption. After field-programmable gate array technology and standard cell mapping, by selecting area as the optimal cost implementation, we obtain average improvements in the actual area of 16.33% and 2.27%, respectively.

基于多策略量子狼群算法的混合极性Reed–Muller逻辑电路多目标优化

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