Probabilistic Evaluation of Solutions in Variability-Driven Optimization

VLSI design optimization requires evaluation of di#erent solutions, to compare superiority of one over the other. Typically, a solution is superior if it has a better associated timing and cost. In the presence of fabrication variability, the timing and cost of a solution become random variables with spatial and functional correlations. Therefore the evaluation of solutions shall be performed probabilistically to determine the probability that a solution has better cost and timing. In this paper we propose and evaluate three methods for fast and accurate probabilistic comparison of solutions: 1) regular Monte Carlo simulation (as a basis of comparison), 2) joint-pdf approximation using moment matching, and 3) bound-based Conditional Monte Carlo simulation. We integrated these methods in a variability-driven leakage optimization framework using dual threshold voltages. Experimental results show that joint-pdf based approximation is very fast, however it results in sub-optimal solutions due to lower accuracy. Conditional Monte Carlo method is on average 25 times faster than regular Monte Carlo, but slower than approximating jointpdf. It also results in additional improvement in expected leakage, when compared to joint-pdf method. Monte Carlo simulation is extremely slow and inapplicable to an optimization framework. Deterministic approaches that are based on worst-case estimates had the highest expected leakage