Clustering based pruning for statistical criticality computation under process variations

We present a new linear time technique to compute criticality information in a timing graph by dividing it into "zones". Errors in using tightness probabilities for criticality computation are dealt with using a new clustering based pruning algorithm which greatly reduces the size of circuit-level cutsets. Our clustering algorithm gives a 150X speedup compared to a pairwise pruning strategy in addition to ordering edges in a cutset to reduce errors due to Clark's MAX formulation. The clustering based pruning strategy coupled with a localized sampling technique reduces errors to within 5% of Monte Carlo simulations with large speedups in runtime.