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 circuitlevel 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.

• Type: Conference
• Year: 2007
• Where: ICCAD
• Authors: Hushrav Mogal, Haifeng Qian, Sachin S. Sapatnekar, Kia Bazargan
• Keywords: Clustering Algorithm, Hardware, ICCAD 2007, Pairwise Pruning Strategy, Technique Reduces Errors