一种鲁棒的基于模的矩阵分裂迭代混合单元高度电路合法化方法

Jianli Chen, Ziran Zhu, Wen-xing Zhu, Yao-Wen Chang
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引用次数: 5

摘要

现代电路通常包含不同排高的标准单元,以满足各种设计要求。更高的电池以更大的面积和功率为代价,提供更大的驱动强度和更高的速度。多行高度标准单元为布局设计带来了挑战,特别是异构单元结构下的混合单元高度合法化问题。考虑到全局布局的良好单元位置,本文提出了一种鲁棒的基于模的矩阵分裂迭代方法(RMMSIM)来解决混合单元高度合法化问题。该方法在确定全局单元排序和放宽右边界约束的前提下,首先将问题转化为等效线性互补问题(LCP),然后对LCP中的矩阵进行适当分割,使RMMSIM能够最优求解LCP。RMMSIM有效地挖掘了电路的稀疏特性,每次迭代只需要线性时间;因此,它可以非常有效地解决QP问题。最后,使用非法单元的分配方案将这些单元与行上的放置位置对齐,并固定超出右边界的单元的放置位置(如果有的话)。实验结果表明了该算法的有效性和高效性。此外,从理论上证明了RMMSIM的收敛性和最优性,并进行了实证验证。针对各种需要高效求解大规模凸二次规划问题的优化问题,本文提出了一种新的RMMSIM公式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Robust Modulus-Based Matrix Splitting Iteration Method for Mixed-Cell-Height Circuit Legalization
Modern circuits often contain standard cells of different row heights to meet various design requirements. Taller cells give larger drive strengths and higher speed at the cost of larger areas and power. Multi-row height standard cells incur challenging issues for layout designs, especially the mixed-cell-height legalization problem with heterogeneous cell structures. Honoring the good cell positions from global placement, we present in this article a robust modulus-based matrix splitting iteration method (RMMSIM) to solve the mixed-cell-height legalization problem. Fixing the cell ordering from global placement and relaxing the right-boundary constraints, our proposed method first converts the problem into an equivalent linear complementarity problem (LCP), and then properly splits the matrices in the LCP so that the RMMSIM can solve the LCP optimally. The RMMSIM effectively explores the sparse characteristic of a circuit, and takes only linear time per iteration; as a result, it can solve the QP very efficiently. Finally, an allocation scheme for illegal cells is used to align such cells to placement sites on rows and fix the placement of out-of-right-boundary cells, if any. Experimental results show the effectiveness and efficiency of our proposed algorithm. In addition, the RMMSIM convergence and optimality are theoretically proved and empirically validated. In particular, this article provides a new RMMSIM formulation for various optimization problems that require solving large-scale convex quadratic programming problems efficiently.
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