摘要
在计算机辅助逻辑设计理论中,最小项(零多维体)的相邻度是一个基本且十分有用的概念,一系列重要的二级优化算法都是以最小项相邻度概念为基础的.本文把最小项相邻度的概念推广到任意维多维体,定义了多维体方向集概念.并利用方向集概念成功地提出了优于张弛算法的二级优化算法:有限搜索法,即有限搜索法在造价优于张弛法的前提下,求取质蕴涵项的速度比张弛法快一到几个数量级,且有限搜索法扩展了张弛法的适用范围,可以综合输入变量大于20的布尔函数.
The concept of adjacency degree of zero degree cube is adapted to highdegree cube, and a new concept 'Direction set' for high degree cube is defined. The concept is used to successfully put forth a new two level logic minimization algorithm-SLA. which is better than the Relaxation algorithm, e.g., when the cost of result of our algorithm is cheaper than that of Relaxation, the speed of finding the largest implicant covering a cube of cur algorithm is ten to thousand times faster than that of Relaration. SLA can be used for synthesis of high input variables (n>20) Boolean functions.
出处
《计算机学报》
EI
CSCD
北大核心
1991年第11期871-875,共5页
Chinese Journal of Computers
关键词
计算机
二级优化
逻辑设计
算法
Logic synthesis, prime implicant, consensus, star product(*), sharp product (#), relaxation algorithm, shrink algorithm
