March 14, 2024, 4:41 a.m. | Yuyang Ye, Peng Xu, Lizheng Ren, Tinghuan Chen, Hao Yan, Bei Yu, Longxing Shi

cs.LG updates on arXiv.org arxiv.org

arXiv:2403.08193v1 Announce Type: new
Abstract: Gate sizing plays an important role in timing optimization after physical design. Existing machine learning-based gate sizing works cannot optimize timing on multiple timing paths simultaneously and neglect the physical constraint on layouts. They cause sub-optimal sizing solutions and low-efficiency issues when compared with commercial gate sizing tools. In this work, we propose a learning-driven physically-aware gate sizing framework to optimize timing performance on large-scale circuits efficiently. In our gradient descent optimization-based work, for obtaining …

abstract arxiv commercial cs.ar cs.et cs.lg design efficiency gate low machine machine learning multiple optimization role scale solutions tools type

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