MPGA: An Evolutionary State Assignment for Dynamic and Leakage Power reduction at FSM synthesis
MPGA: An Evolutionary State Assignment for Dynamic and Leakage Power reduction at FSM synthesis
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MPGA：FSM 合成时动态和泄漏功率降低的进化状态分配
DOI：
10.1049/iet-cdt.2016.0199
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发表时间：
2018
影响因子：
1.2
通讯作者：
Qinyu Wang
中科院分区：
文献类型：
--
作者：
Yanyun Tao;Yuzhen Zhang;Qinyu Wang
随着深亚微米和纳米技术的发展，最小化漏电流功耗作为动态功耗降低在IC设计中变得非常重要。为了实现有限状态机（FSM）综合的低功耗状态分配，提出了一种基于多种群遗传算法（MPGA）的状态分配方法. MPGA由一个外循环和一组内GA组成。在MPGA中，内部遗传算法是一个局部搜索组件，用于寻找低功耗状态分配，选择、交叉和变异用于个体变异，代价函数基于互补金属氧化物半导体（CMOS）门的功耗公式定义，用于动态功耗和泄漏功耗估计.外循环通过种群变异模式、种内竞争和新生儿来优化内部遗传算法的参数。采用23个常用的有限状态机作为基准，对MPGA的有效性进行了测试，并对不同的状态分配方法进行了比较。实验结果表明，MPGA在动态功耗和泄漏功耗降低方面都取得了显著的进步。
As the development of deep‐submicron and nano‐technology, leakage power minimisation becomes asimportant as dynamic power reduction in IC design. In order to achieve low‐powerstate assignment for finite‐state machine (FSM) synthesis, a multi‐populationgenetic algorithm (MPGA)‐based state assignment method isproposed. MPGA consists of an outer‐loop and a set of inner‐GAs. In MPGA,inner‐GA is a local search component for finding low‐power state assignment.Selection,crossoverandmutationare used to perform variations on individuals.Cost function is defined based on power dissipation formulation of complementarymetal oxide semiconductor (CMOS) gate for dynamic power andleakage power estimation. The outer‐loop is used to optimise the parameters ofinner‐genetic algorithm (GA) throughpopulationvariation schema,intra‐specific competitionandnewborn. Twenty‐three FSMs that were commonly used asbenchmarks are employed to test the effectiveness of MPGA and compare differentstate assignment methods. Experimental results show MPGA achieves a significantimprovement over the previous publications both on dynamic power and leakagepower reduction in most benchmarks.