1. 北京大学计算机科学技术研究所,北京,100871
2. 杭州电子工业学院CAD所,杭州,310037
3. 北京大学计算机科学技术研究所!北京100871杭州电子工业学院CAD所,杭州,310037
纸质出版:1999
移动端阅览
[1]胡卫明,吴兵,李翠超,严晓浪.自组织神经网络在时延驱动的MCM系统划分中的应用[J].电子学报,1999(11):124-126.
胡卫明, 吴兵, 李翠超, et al. Application of Self Organizing Neural Network in Timing Driven System Partitioning on MCM[J]. Acta Electronica Sinica, 1999, (11).
MCM是集成电路中的一种新技术.划分是MCM设计中极其重要的一个环节.本文应用Kohonen自组织神经网络求解以面积和时延为约束的、以芯片之间的连线代价和系统时钟周期为优化目标的MCM系统划分问题.算法用单元之间的联接度和组合逻辑单元的内部时延表示直接相联单元间的相似性
并应用模糊相似性变换建立间接相联单元间的相似性.算法将各单元映射到二维平面上
对应一个或者多个神经元.学习过程是通过单元之间有协作的移动
使相似性大的单元能够逐渐移到一起来完成的.
MCM is a new technology of ICs.In the design steps of MCM
partitioning is very important.Based on the improvement to Kohonen self organizing neural nerwork
the neural network approach for the timing driven system partitioning on MCM is presented.In the algorithm
the total routing cost between the chips and the circle time are both minimized
while satisfying area and timing constraints.The similarity of the directly connected cells is described by the connection weight between the cells and the internal delay of the combinatorial cell
and fuzzy similarity computing is applied to describing the similarity of indirectly connected cells.Each cell is mapped noto a two dimensional plane
and corresponds to one or a few neurons.The neural network is learned from a sequence of movements of cells
allows corresponding cells to cooperate during the learning process.By the self organizing learning process
the cells that have great similarity with each other will move as closely as possible.
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