Electrical & Computer Engineering     |     Carnegie Mellon

Tuesday, September 19, 12:00-1:00 p.m. HH-1112


Xin Li
Carnegie Mellon University

Projection-Based Statistical Analysis of Full-Chip Leakage Power with Non-Log-Normal Distributions

In this talk we discuss a novel projection-based algorithm to estimate the full-chip leakage power with consideration of both inter-die and intra-die process variations. Unlike many traditional approaches that rely on log-Normal approximations for such estimations, the proposed algorithm applies a novel projection method to extract a low-rank quadratic model of the logarithm of the full-chip leakage current and, therefore, is not limited to log-Normal distributions. By exploring the underlying sparse structure of the problem, an efficient algorithm is developed to extract the non-log-Normal leakage distribution with linear computational complexity in circuit size. In addition, an incremental analysis algorithm is proposed to quickly update the leakage distribution after changes to a circuit are made. Our numerical examples in a commercial 90nm CMOS process demonstrate that the proposed algorithm provides 4x error reduction compared with the previously proposed log-Normal approximations, while achieving orders of magnitude more efficiency than a Monte Carlo analysis with 104 samples.


Xin Li received the Ph.D. degree in Electrical and Computer Engineering from Carnegie Mellon University, Pittsburgh, PA in 2005. He was a summer intern at Extreme DA, Palo Alto, CA, in 2004. Since the summer of 2005, he has been a systems scientist in the Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA. His current research interests include modeling, simulation and synthesis for analog/RF and digital systems. Dr. Xin Li served on the IEEE Outstanding Young Author Award Selection Committee in 2006. He received the IEEE/ACM William J. McCalla ICCAD Best Paper Award in 2004.