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EMPIRE: An Efficient and Compact Multiple-Parameterized Model-Order Reduction Method for Physical Optimization

机译:EMPIRE:一种有效且紧凑的多参数模型降阶方法,用于物理优化

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摘要

Parameterized model-order reduction is useful for very large-scale integration VLSI physical design and optimization. In this paper, we propose an efficient yet accurate parameterized model-order reduction method EMPIRE for multiple parameters. It uses implicit moment matching to efficiently handle high-order moments of a large number of parameters. In addition, it can match the moments of different parameters with different accuracy according to their influence on the objective under study, and such influence is measured by the 2-norm of their coefficient matrix in the canonical form. It develops three algorithms to further suppress the size of the reduced model by finding a projection matrix that has a much smaller number of columns than the original one. Experimental results show that compared with the best existing algorithm CORE that uses explicit moment matching for the parameters, EMPIRE reduces waveform error by 47.8 × at a similar runtime.
机译:参数化模型阶数减少对于超大规模集成VLSI物理设计和优化非常有用。在本文中,我们针对多个参数提出了一种有效而准确的参数化模型阶约简方法EMPIRE。它使用隐式矩匹配来有效处理大量参数的高阶矩。此外,它还可以根据不同参数对研究对象的影响,以不同的精度匹配它们的力矩,并通过规范形式以其系数矩阵的2范数来衡量这种影响。它开发了三种算法,通过找到投影矩阵的列数比原始矩阵少得多的投影矩阵来进一步抑制简化模型的大小。实验结果表明,与使用显式矩匹配的参数的最佳现有算法CORE相比,EMPIRE在类似的运行时间下可将波形误差降低47.8×。

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