On Conventional and Machine Learning Assisted High Sigma Analysis

Speaker: Wei Wu
Affiliation: Ph.D. Candidate - UCLA

Abstract:  Statistical circuit simulation exhibits increasing importance for circuit designs under process variations. In particular, high sigma analysis is needed to optimize highly-duplicated standard cells, where an extremely rare circuit failure event could lead to catastrophe of the entire chip. Conventional importance sampling (IS) approaches perform high sigma analysis efficiently at low dimensionality, but perform poorly either when there are a larger number of process variation variables, or when the failing samples are distributed in multiple regions.

In this dissertation, a series of high sigma analysis approaches have been proposed. First, a high dimensional importance sampling (HDIS) is presented to mitigate the dimensionality problem in traditional IS. Moreover, two machine learning assisted approaches are proposed for high sigma analysis. The rare-even microscope (REscope) trains classifier(s) to filter out the majority of the unlikely-to-fail samples and surgically look into those likely-to-fail ones, whose distribution is analytically modeled as a generalized pareto distribution to estimate failure probability. Finally, hyperspherical clustering and sampling (HSCS) algorithm is proposed to cluster failing samples and to perform importance sampling around those clusters to cover all failure regions. Experiment results demonstrate that the proposed approaches are 2-3 orders faster than Monte Carlo, and more accurate than both academia solutions such as IS, Markov Chain Monte Carlo, and industrial solutions such as mixture IS used by ProPlus Design Automation, Inc.

Biography:  Wei Wu received his BS and MS degrees in Electrical Engineering from Beihang University, China. In the winter of 2012, he joined Prof. Lei He’s design automation lab in the Electrical Engineering Department at UCLA to pursue his Ph.D. degree. His research interests include circuit simulation, statistical circuit analysis, and FPGA based energy efficient computing systems (e.g. deep learning inference on FPGA, analog circuit emulation on FPGA). During his research at UCLA, he has authored over 10 research papers in top-tier journals and peer-reviewed conferences.

For more information, contact Prof. Lei He ()

Date(s) - Mar 16, 2016
11:30 am - 1:30 pm

E-IV Maxwell Room #57-124
420 Westwood Plaza - 5th Flr. , Los Angeles CA 90095