Enhancing the performance of multi-cycle path analysis in an industrial setting

In this paper we enhance the performance of multi-cycle path analysis in an industrial setting. Industrial designs are, in general, more complicated, but contain more information than fundamental sequential circuits. We show how such information is used for improving the quality and the efficiency of multi-cycle path analysis. Specifically, we propose local FSM learning to take into account reachability information. We also propose FF enable learning to accelerate multi-cycle path analysis. Experimental results show that our methods can handle large industrial designs with tens of thousands of FFs and detects more multi-cycle paths faster than conventional ones.

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