TY - JOUR
AU - Wang, Hai
AB -  Statistical Rare-Event Analysis and Parameter Guidance by Elite Learning Sample Selection YUE ZHAO, TAEYOUNG KIM, HOSOON SHIN, and SHELDON X.-D. TAN, University of California, Riverside XIN LI, Carnegie Mellon University HAIBAO CHEN, Shanghai Jiao Tong University HAI WANG, University of Electronic Science and Technology of China Accurately estimating the failure region of rare events for memory-cell and analog circuit blocks under process variations is a challenging task. In this article, we propose a new statistical method, called EliteScope, to estimate the circuit failure rates in rare-event regions and to provide conditions of parameters to achieve targeted performance. The new method is based on the iterative blockade framework to reduce the number of samples, but consists of two new techniques to improve existing methods. First, the new approach employs an elite-learning sample-selection scheme, which can consider the effectiveness of samples and well coverage for the parameter space. As a result, it can reduce additional simulation costs by pruning less effective samples while keeping the accuracy of failure estimation. Second, the EliteScope identifies the failure regions in terms of parameter spaces to provide a good design guidance to accomplish the performance target. It applies variance-based feature selection to find the 
TI - Statistical Rare-Event Analysis and Parameter Guidance by Elite Learning Sample Selection
JO - ACM Transactions on Design Automation of Electronic Systems (TODAES)
DO - 10.1145/2875422
DA - 2016-05-27
UR - https://www.deepdyve.com/lp/association-for-computing-machinery/statistical-rare-event-analysis-and-parameter-guidance-by-elite-zGelnJqSUP
SP - 1
VL - 21
IS - 4
DP - DeepDyve
ER -