基于量子遗传算法的XOR/AND电路功耗和面积优化

Power and area optimization of XOR/AND circuits based on quantum genetic algorithm

通过研究量子遗传算法、XOR/AND逻辑展开式及其对应电路的功耗和面积关系,提出一种基于量子遗传算法的单输出XOR/AND电路功耗和面积同时优化的算法.从量子比特、量子叠加态的概念出发,结合XOR/AND电路的功耗估计模型,以XOR/AND门电路数衡量电路面积,利用染色体编码、适应度函数构造和量子旋转门调整等方法,有效实现了功耗和面积的折中.将提出算法与遍历算法和整体退火遗传算法进行比较,结果表明该算法高效、稳定、收敛速度快.对较大规模电路的测试结果表明,该算法的优化结果与极性为零时的XOR/AND电路相比,功耗和面积平均节省了81.7%和54.7%.

By studying quantum genetic algorithm, XOR/AND logic expansions and the relationship of circuit power dissipation and area, this work proposed an algorithm based on quantum genetic algorithm to simultaneously optimize the power dissipation and area of single output XOR/AND circuits. This algorithm uses the power estimation model of XOR/AND circuits, applies the number of XOR/AND gate circuits to measure the circuit area, and combines with qubit chromosomes, fitness function and adjustable strategy of rotation angle, which under the concepts of quantum bits and quantum superposition. This algorithm implements the performance trade off of power dissipation and area effectively. Compared with the traversal search algorithm and the whole annealing genetic algorithms, this algorithm has high efficiency, good stability, and especially fast convergence. The optimization results shows 81.7% and 54.7% averagely savings of the power dissipation and area of the XOR/AND circuits under the best polarity which is searched by the proposed algorithm, compared to those of the XOR/AND circuits under polarity zero in the large-scale test circuit verification.