Placement initialization via a projected eigenvector algorithm: late breaking results
Placement initialization via a projected eigenvector algorithm: late breaking results
复制标题
通过投影特征向量算法进行布局初始化：最新成果
DOI：
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Yucheng Wang
中科院分区：
文献类型：
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作者：
Pengwen Chen;Chung;Albert Chern;Chester Holtz;Aoxi Li;Yucheng Wang
VLSI设计分析放置的规范方法依赖于求解非线性程序来最大程度地减少电线长度和细胞重叠。我们专注于产生初始布局，以便与现有的初始化启发式方法相比，全局分析贴合器的性能更好。我们将初始化问题减少到四二次约束二次程序。我们的配方意识到固定宏。我们提出了一种有效的算法，该算法可以快速生成具有数百万个单元的测试算盘的初始化。我们表明，我们的参数初始化方法相对于详细的放置线长度可以表现出色。
Canonical methods for analytical placement of VLSI designs rely on solving nonlinear programs to minimize wirelength and cell overlap. We focus on producing initial layouts such that a global analytical placer performs better compared to existing heuristics for initialization. We reduce the problem of initialization to a quadratically constrained quadratic program. Our formulation is aware of fixed macros. We propose an efficient algorithm which can quickly generate initializations for testcases with millions of cells. We show that the our method for parameter initialization results in superior performance with respect to post-detailed placement wirelength.