Timing budgeting under arbitrary process variations

Timing budgeting under process variations is an important step in a statistical optimization flow. We propose a novel formulation of the problem where budgets are statistical instead of deterministic as in existing works. This new formulation considers the changes of both the means and variances of delays, and thus can reduce the timing violation introduced by ignoring the changes of variances. We transform the problem to a linear programming problem using a robust optimization technique. Our approach can be used in late-stage design where the detailed distribution information is known, and is most useful in early-stage design since our approach does not assume specific underlying distributions. In addition, with the help of block-level timing budgeting, our approach can reduce the timing pessimism. Our approach is applied to the leakage power minimization problem. The results demonstrate that our approach can reduce timing violation from 690 ps to Ops, and the worst total leakage power by 17.50% on average.