电子学报 ›› 2016, Vol. 44 ›› Issue (5): 1202-1207.DOI: 10.3969/j.issn.0372-2112.2016.05.027

• 学术论文 • 上一篇    下一篇

基于多策略离散粒子群算法的MPRM电路延时与面积优化

符强1,2, 汪鹏君1, 童楠2, 王铭波2, 张会红1   

  1. 1. 宁波大学电路与系统研究所, 浙江宁波 315211;
    2. 宁波大学科学技术学院, 浙江宁波 315212
  • 收稿日期:2015-12-09 修回日期:2016-02-04 出版日期:2016-05-25 发布日期:2016-05-25
  • 通讯作者: 汪鹏君
  • 作者简介:符强 男,1975年出生,江西赣州人.博士研究生,讲师,主要研究方向为低功耗集成电路理论及优化设计.E-mail:fuqiang@nbu.edu.cn
  • 基金资助:

    国家自然科学基金(No.61306041,No.61234002);宁波市自然科学基金(No.2014A610069);浙江省教育厅科研项目(No.Y201326770)

Delay and Area Optimization for MPRM Circuits Based on Multi-strategy Discrete Particle Swarm Optimization

FU Qiang1,2, WANG Peng-jun1, TONG Nan2, WANG Ming-bo2, ZHANG Hui-hong1   

  1. 1. Institute of Circuits and Systems, Ningbo University, Ningbo, Zhejiang 315211, China;
    2. College of Science and Technology, Ningbo University, Ningbo, Zhejiang 315212, China
  • Received:2015-12-09 Revised:2016-02-04 Online:2016-05-25 Published:2016-05-25

摘要:

针对大规模混合极性Reed-Muller(Mixed Polarity Reed-Muller,MPRM)逻辑电路的延时与面积优化,提出一种基于多策略离散粒子群优化(Multi-Strategy Discrete Particle Swarm Optimization,MSDPSO)的极性搜索方法.在MSDPSO算法中,对粒子进行团队划分,每个团队既执行不同策略,又相互联系,并行完成探索与开发的双重任务.同时在进化过程中采用高斯调整来激活寻优能力较差的粒子.结合MSDPSO算法和列表极性转换技术,对大规模MPRM电路进行延时与面积极性搜索.最后对PLA格式的MCNC Benchmark电路进行算法性能测试,结果验证了MSDPSO算法的有效性.与离散粒子群优化(Discrete Particle Swarm Optimization,DPSO)算法的优化结果相比较,MSDPSO算法获取的电路延时平均缩短8.43%,面积平均节省38.36%.

关键词: 多策略离散粒子群算法, MPRM逻辑电路, 延时与面积优化, 极性搜索

Abstract:

In order to improve the delay and area design of large-scale MPRM circuits, the multi-strategy discrete particle swarm optimization(MSDPSO)is proposed.In MSDPSO, the particles were divided into several teams with different strategy, and each team cooperated with others to promote the exploration and exploitation of the particle population.Meanwhile, the Gaussian adjustment was adopted to activate the worse individuals.Combined with MSDPSO and tabular technique, the best polarity of delay and area was searched for large-scale MPRM circuits.MCNC Benchmarks with PLA format are tested to verify the effectiveness of the MSDPSO, and the results show that MSDPSO has achieved an average saving of 8.46% and 38.73% on delay and area respectively in comparison with the DPSO.

Key words: multi-strategy discrete particle swarm optimization(MSDPSO), MPRM circuits, delay and area optimization, polarity search

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