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浙江大学学报(工学版)
J4  2009, Vol. 43 Issue (11): 1982-1987    DOI: 10.3785/j.issn.1008-973X.2009.11.007
自动化技术、计算机技术     
基于量子遗传算法的XOR/AND电路功耗和面积优化
汪鹏君1,2,3,吴文晋1,张小颖2,王伶俐2,陈耀武3
（1.宁波大学 电路与系统研究所,浙江 宁波 315211；
2.复旦大学 专用集成电路与系统国家重点实验室,上海 201203；
3.浙江大学 数字技术及仪器研究所,浙江 杭州 310027）
Power and area optimization of XOR/AND circuits based on quantum genetic algorithm
WANG Peng-jun1,2,3, WU Wen-jin1, ZHANG Xiao-ying2, WANG Ling-li2, CHEN Yao-wu3
(1. Institute of Circuits and Systems, Ningbo University, Ningbo 315211, China;
2. State Key Laboratory of ASIC and System, Fudan University, Shanghai 201203, China;
3. Institute of Advanced Digital Technologies and Instrumentation, Zhejiang University, Hangzhou 310027, China)
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摘要：

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％.
Abstract:

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．
出版日期: 2009-11-01
:  TP 18  
基金资助:

国家自然科学基金资助项目（60676020,60776022）；浙江省科技计划资助项目（2008C21166）；宁波大学胡岚优秀博士基金资助项目.
通讯作者: 王伶俐，男，副教授.     E-mail: llwang@fudan.edu.cn
作者简介: 汪鹏君（1966-），男，浙江奉化人，教授，从事多值逻辑电路和低功耗集成电路研究.
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引用本文:

汪鹏君, 吴文晋, 张小颖, 等. 基于量子遗传算法的XOR/AND电路功耗和面积优化[J]. J4, 2009, 43(11): 1982-1987.

HONG Feng-Jun, TUN Wen-Jin, ZHANG Xiao-Ying, et al. Power and area optimization of XOR/AND circuits based on quantum genetic algorithm. J4, 2009, 43(11): 1982-1987.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2009.11.007    或    http://www.zjujournals.com/eng/CN/Y2009/V43/I11/1982

[1] TAN E C, YANG H. Optimization of fixed-polarity Reed-Muller circuits using dual-polarity property [J]. Circuits, Systems, and Signal Process, 2000, 19(6): 535-548．
[2] HAN K H, KIM J H. Genetic quantum algorithm and its application to combination optimization problems [C]∥ IEEE Proceedings of the 2000 Congress on Evolution Compotation. San Diego: IEEE, 2000: 1354-1360.
[3] HAN K H, KIM J H. On setting the parameters of QEA for practical application: some guidelines based on empirical evidence [C]∥Genetic and Evolutionary Computation Conference. Chicago, Berlin， Heidelberg: Springer-Verlag, 2003: 427-428．
[4] VLACHOGIANNIS J G, QSTERGAARD J. Reactive power and voltage control based on general quantum genetic algorithms [J]. Expert Systems with Applications, 2009, 36(3): 6118-6126．
[5] 周露芳,古乐野. 基于量子遗传算法的二维最大熵图像分割[J]. 计算机应用, 2005, 25(8): 1805-1807．
ZHOU Lu-fang, GU Le-ye. 2-D maximum entropy method in image segmentation based on genetic quantum algorithm [J]. Computer Application, 2005, 25(8): 1805-1807．
[6] 汪鹏君,陆金刚. 基于XNOR/OR逻辑的低功耗最佳极性搜索[J]. 电子学报, 2008, 36(5): 993-997．
WANG Peng-jun, LU Jin-gang. Searching the best polarity for low power dissipation based on XNOR/OR logic [J]. Acta Electronica Sinica, 2008, 36(5): 993-997．
[7] ZHOU H, WONG D F. Optimal low power XOR gate decomposition [C]∥ACM/IEEE Design Automation Conference. Las Angeles: IEEE, 2000: 104-107．
[8] 张葛祥,金炜东. 量子遗传算法的改进及其应用[J]. 西南交通大学学报, 2003, 38(6): 717-722．
ZHANG Ge-xiang, JIN Wei-dong. Improvement of quantum genetic algorithm and its application [J]. Journal of Southwest Jiaotong University, 2003, 38(6): 717-722．
[9] WANG L, ALMAINI A E A. Efficient polarity conversion for large Boolean functions [J]. IEE Proceedings of Computers and Digital Techniques, 1999, 146(4): 197-204．
[10] WANG Peng-jun, LU Jin-gang, CHEN Ken, et al. Low power polarity conversion based on whole annealing genetic algorithm [J] Journal of Semiconductors, 2008, 29(2): 298-303.
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