Title :
PiCAP: A parallel and incremental capacitance extraction considering stochastic process variation
Author :
Gong, Fang ; Yu, Hao ; He, Lei
Author_Institution :
EE Dept., UCLA, Los Angeles, CA, USA
Abstract :
It is unknown how to include stochastic process variation into fast multipole method (FMM) for a full chip capacitance extraction. This paper presents a parallel FMM extraction using stochastic polynomial expanded geometrical moments. It utilizes multiprocessors to evaluate in parallel for the stochastic potential interaction and its matrix vector product (MVP) with charge. Moreover, a generalized minimal residual (GMRES) method with deflation is modified to incrementally consider the nominal value and the variance. The overall extraction flow is called piCAP. Experiments show that the parallel MVP in piCAP is up to 3X faster than the serial MVP, and the incremental GMRES in pi-CAP is up to 15X faster than non-incremental GMRES methods.
Keywords :
integrated circuit design; stochastic processes; GMRES method; expanded geometrical moment; generalized minimal residual method; incremental capacitance extraction; matrix vector product; multiprocessor system; nominal value; parallel FMM extraction; parallel capacitance extraction; parallel fast multipole method extraction; stochastic polynomial; stochastic potential interaction; stochastic process variation; Algorithm design and analysis; Capacitance; Computational efficiency; Design automation; Dielectrics; Helium; Matrix decomposition; Permission; Polynomials; Stochastic processes; Capacitance extraction; Incremental precondition; Parallel fast-multipole method; Stochastic geometrical moments;
Conference_Titel :
Design Automation Conference, 2009. DAC '09. 46th ACM/IEEE
Conference_Location :
San Francisco, CA
Print_ISBN :
978-1-6055-8497-3