基于MODPSO算法的FPRM电路多约束极性优化方法

符强; 汪鹏君; 童楠; 王铭波; 张会红

doi:10.11999/JEIT160458

基于MODPSO算法的FPRM电路多约束极性优化方法

doi: 10.11999/JEIT160458

2.
(宁波大学电路与系统研究所宁波 315211) ②(宁波大学科学技术学院宁波 315212)

基金项目:

国家自然科学基金(61306041, 61234002)，十二五浙江省高校重点学科计算机应用技术，浙江省教育厅科研项目(Y201326770)，宁波市自然科学基金(2014A610069，2015A610107)

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出版历程
- 收稿日期: 2016-05-05
- 修回日期: 2016-10-18
- 刊出日期: 2017-03-19

Multi-constrained Polarity Optimization of Large-scale FPRM Circuits Based on Multi-objective Discrete Particle Swarm Optimization

2.
(Institute of Circuits and Systems, Ningbo University, Ningbo 315211, China)

Funds:

The National Natural Science Foundation of China (61306041, 61234002), The Twelfth Five-Year Plan of Zhejiang Province Key Discipline (Computer Application Technology), The Scientific Research Fund of Zhejiang Provincial Education Department (Y201326770), The Ningbo Natural Science Foundation (2014A610069, 2015A610107)

摘要

摘要: 为求解较大规模FPRM逻辑电路中多约束条件下的极性优化问题，该文提出一种基于多目标离散粒子群优化(Multi-Objective Discrete Particle Swarm Optimization, MODPSO)算法的求解方法。首先针对FPRM电路极性设计需要满足延时短、面积小的多约束要求，构建了多目标决策模型。然后结合极性转换算法和MODPSO算法，对电路进行最优极性搜索，以获取电路延时和面积的Pareto最优解集。最后利用17个MCNC Benchmark电路进行测试，并将MODPSO算法与DPSO算法、NSGA-II算法进行实验对比，结果验证了算法的有效性。
- FPRM逻辑电路 /
- 延时与面积优化 /
- 极性搜索 /
- Pareto /
- 多目标离散粒子群算法
Abstract: For multi-constrained polarity optimization of large-scale FPRM circuits, a Multi-Objective Discrete Particle Swarm Optimization (MODPSO) algorithm is proposed. Firstly, the multi-objective decision model is established according to the delay-area trade-off of large-scale FPRM circuits. Secondly, combined with tabular technique and MODPSO, the best polarities of delay and area are searched for large-scale FPRM circuits, to obtain the Pareto optimal set for delay and area. Finally, the algorithm MODPSO is compared with the algorithm DPSO and NSGA-II on MCNC Benchmarks with PLA format, and the results verify the effectiveness of the MODPSO.
- FPRM circuits /
- Delay-area trade-off /
- Polarity search /
- Pareto /
- Multi-Objective Discrete Particle Swarm Optimization (MODPSO)

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参考文献(21)

MONREIRO C, TAKAHASHI Y, and SEKINE T. Low- power secure S-box circuit using charge-sharing symmetric adiabatic logic for advanced encryption standard hardware design[J]. IET Circuits, Devices Systems, 2015, 9(5): 362-369.

王伦耀, 夏银水, 陈偕雄. 逻辑函数的双逻辑综合与优化[J]. 计算机辅助设计与图形学学报, 2012, 24(7): 961-967.

WANG Lunyao, XIA Yinshui, and CHEN Xiexiong. Logic synthesis and optimization based on dual logic[J]. Journal of Computer-Aided Design Computer Graphics, 2012, 24(7): 961-967.

卜登立, 江建慧. 基于混合多值离散粒子群优化的混合极性 Reed-Muller 最小化算法[J]. 电子与信息学报, 2013, 35(2): 361-367. doi: 10.3724/SP.J.1146.2012.00790.

BU Dengli and JIANG Jianhui. Hybrid multi-valued discrete particle Swarm optimization algorithm for mixed-polarity Reed-Muller minimization[J]. Journal of Electronics Information Technology, 2013, 35(2): 361-367. doi: 10.3724/ SP.J.1146.2012.00790.

王振海, 汪鹏君, 俞海珍, 等. 基于PSO算法的FPRM电路延时和面积优化[J]. 电路与系统学报, 2012, 17(5): 75-80.

WANG Zhenhai, WANG Pengjun, YU Haizhen, et al. Delay and area optimization for FPRM circuits based on PSO algorithm[J]. Journal of Circuits and Systems, 2012, 17(5): 75-80.

WANG Pengjun, LI Kangping, and ZHANG Huihong. PMGA and its application in area and power optimization for ternary FPRM circuit[J]. Journal of Semiconductors, 2016, 37(1): 015007.

DAS A and PRADHAN S N. Thermal aware FPRM based AND-XOR network synthesis of logic circuits[C]. IEEE 2nd International Conference on Recent Trends in Information System(ReTIS). IEEE, Kolkata, India, 2015: 497-502. doi: 10.1109/ReTIS.2015.7232930.

姜兴龙, 梁广, 刘会杰, 等. 一种新型的低轨存储转发通信星座设计方法[J]. 电子与信息学报, 2014, 36(3): 676-682. doi: 10.3724/SP.J.1146.2013.00551.

JIANG Xinglong, LIANG Guang, LIU Huijie, et al. A new design method of store and forward LEO communication satellite constellation[J]. Journal of Electronics Information Technology, 2014, 36(3): 676-682. doi: 10.3724/ SP.J.1146.2013.00551.

ESFAHANI I J, YOO C K, KALOGIROU SOTERIS A, et al. An optimization algorithm-based pinch analysis and GA for an off-grid batteryless photovoltaic-powered reverse osmosis desalination system[J]. Renewable Energy, 2016, 91: 233-248.

SRIVASTAV A and AGRAWAL S. Multi-objective optimization of hybrid backorder inventory model[J]. Expert Systems with Applications, 2016, 51: 76-84.

MARTINEZ-VARGAS A, DOMINGUEZ-GUERRERO J, ANDRADE G, et al. Application of NSGA-II algorithm to the spectrum assignment problem in spectrum sharing networks[J]. Applied Soft Computing, 2016, 39: 188-198.

YUAN Y, XU H, WANG B, et al. A new dominance relation-based evolutionary algorithm for many-objective optimization[J]. IEEE Transactions on Evolutionary Computation, 2016, 20(1): 16-37.

HOAI BACH NGUYEN, XUE Bing, LIU L, et al. New mechanism for archive maintenance in PSO-based multi- objective feature selection[J]. Soft Computing, 2016: 1-20. doi: 10.1007/s00500-016-2128-8.

JASSANI B A Al, URQUHART N, and ALMAINI A E A. Manipulation and optimization techniques for Boolean logic[J]. IET Computers and Digital Techniques, 2010, 4(3): 227-239.

汪鹏君, 王振海, 陈耀武, 等. 固定极性Reed-Muller电路最优延时极性搜索[J]. 浙江大学学报: 工学版, 2013, 47(2): 361-366. doi: 10.3785/j.issn.1008-973X.2013.02.026.

WANG Pengjun, WANG Zhenhai, CHEN Yaowu, et al. Searching the best polarity for fixed polarity Reed-Muller circuits based on delay model[J]. Journal of Zhejiang University (Engineering Science), 2013, 47(2): 361-366. doi: 10.3785/j.issn.1008-973X. 2013.02.026.

CORTADELLA J. Timing-driven logic bi-decomposition[J]. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2003, 22(6): 675-685.

AMINBAKHSH S and SONMEZ R. Discrete particle swarm optimization method for the large-scale discrete timecost trade-off problem[J]. Expert Systems with Applications, 2016, 51: 177-185.

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