Efficient Pre-conditional Single-Node SOR Method of Statistical 3D Thermal Analysis for Hot Spots

This paper proposes an efficient statistical method to analyze temperature variations for few hot spots in a 3D thermal analysis. The new method, called EPSN-SOR (and its non-preconditioned version SNSOR), is based on a novel localized relaxation and iterative scheme. The new method can perform statistical analysis on single spot at a time. EPSN-SOR employs evolution and pre-condition techniques for speedup by exploiting topological similarity of two nearby spots. The method further considers spatial correlation in energy and conductance disturbances. Experiments show that EPSNSOR is about three orders of magnitude faster than the Monte-Carlo method with small errors and is about 80X faster than general global SOR (successive over relaxation) method in statistical analysis of 450 hot spots for a test case with 1.3 million nodes (average 0.0553 second per spot) under spatially correlated energy and thermal conductance disturbances on a normal desktop PC.