基于2个阶段遗传算法的MPRM电路面积与SER折中优化

发布时间：2018-03-19 01:26

本文选题：MPRM电路　切入点：面积优化　出处：《计算机辅助设计与图形学学报》2017年10期 　论文类型：期刊论文

【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.
[Abstract]:With the combination of circuit transient fault sensitivity increased in mixed polarity Reed-Muller (MPRM) when it is necessary to optimize circuit soft error rate (SER) as an important constraint, and to obtain high time efficiency circuit area and SER reasonable compromise solutions. Therefore, TPGAHB is proposed with a history buffer the 2 stage genetic algorithm for the area and SER MPRM.TPGAHB circuit trade-off optimization using GA model with the history buffer, 2 stages compromise optimization area and SER. In the first stage in an area of optimized area get optimal solution; second phase using the Pareto optimal principle to quantify the preference area optimal solution as the reference solution calculation of MPRM circuit solution area and SER efficiency factor, and the efficiency factor is optimized, so as to get the SER area and the reasonable compromise solutions. The MCNC circuit optimization results show that the TPGAHB It has better optimization ability. It can get a better area and SER compromise MPRM circuit at higher time efficiency, and it has better applicability to the circuit with more than 14 input.

【作者单位】：井冈山大学电子与信息工程学院;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;同济大学软件学院;
【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)
【分类号】：TN70;TP18

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