Clock network is a vulnerable victim of variations as well as a main power consumer in many integrated circuits. Recently, link-based non-tree clock network attracts people's attention due to its appealing tradeoff between variation tolerance and power overhead. In this work, we investigate how to optimize such clock networks through buffer and wire sizing. A two-stage hybrid optimization approach is proposed. It considers the realistic constraint of discrete buffer/wire sizes and is based on accurate delay models. In order to provide reliable and efficient guidance for the optimization, we suggest to apply support vector machine (SVM)-based machine learning as a surrogate for expensive circuit-level simulation. Experimental results on benchmark circuits show that our sizing method can reduce clock skew by 45% on average with very small increase on power dissipation.
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