摘要
随着组合电路对瞬时故障的敏感度不断增加,在进行混合极性Reed-Muller(MPRM)电路优化时有必要将软错误率(SER)作为一个重要约束,并以较高时间效率获得电路面积与SER的合理折中解.为此,提出一种带历史缓冲区的2个阶段遗传算法TPGAHB来进行MPRM电路面积与SER折中优化.TPGAHB采用带历史缓冲区的GA模型,分2个阶段实施面积与SER折中优化.第1阶段以面积为目标进行优化得到面积最优解;第2阶段利用Pareto最优原理量化偏好将面积最优解作为参考解计算MPRM电路解的面积与SER效率因子,并以效率因子为目标进行优化,从而得到面积与SER的合理折中解.对MCNC电路进行优化的结果表明,TPGAHB具有较好的寻优能力,能够以较高时间效率获得较好的面积与SER折中的MPRM电路,对输入数超过14的电路也有较好适用性.
With the increasing susceptibility of combinational circuits to transient errors,it is essential to consider soft error rate(SER)as one crucial design constraint when optimizing mixed-polarity Reed-Muller(MPRM)circuits,and efficiently obtain reasonable trade-off solutions between area and SER.Two-phase genetic algorithm with history buffer(TPGAHB)is proposed for area and SER optimization of MPRM circuits.TPGAHB performs area and SER trade-off in two phases by using a genetic algorithm(GA)model with history buffer.Phase-one of TPGAHB obtains area minimized solution by optimizing area.Basing on the concept of Pareto optimality,phase-two of TPGAHB quantifies preference by using the area minimized solution as reference and computing efficiency factor of area and SER for MPRM circuits,and obtains reasonable trade-off solutions between area and SER by maximizing the efficiency factor.Experimental results obtained by optimizing several MCNC benchmark circuits show that,TPGAHB has better optimization capability,can obtain MPRM circuits that can provide a good trade-off between area and SER at high time efficiency,and can be applied to circuits with more than14inputs.
作者
卜登立
江建慧
罗文浪
Bu Dengli;Jiang Jianhui;Luo Wenlang(School of Electronics and Information Engineering, Jinggangshan University, Ji’an 343009;Key Laboratory of Watershed Ecology and Geographical Environment Monitoring, National Administration of Surveying, Mapping and Geoinformation of China, Ji’an 343009;School of Software Engineering, Tongji University, Shanghai 201804)
出处
《计算机辅助设计与图形学学报》
EI
CSCD
北大核心
2017年第10期1924-1934,共11页
Journal of Computer-Aided Design & Computer Graphics
基金
国家自然科学基金(61640412
61432017)
流域生态与地理环境监测国家测绘地理信息局重点实验室资助课题(WE2016012
WE2015013)
江西省自然科学基金(20161BAB202048
20171BAB202010)
江西省教育厅科技计划项目(GJJ160746)
