Paper Info
Title
Reversible Gating Architecture for Rare Failure Detection of Analog and Mixed-Signal Circuits
Abstract
Due to the growing complexity and numerous manufacturing variation in safety-critical analog and mixed-signal (AMS) circuit design, rare failure detection in the high-dimensional variational space is one of the major challenges in AMS verification. Efficient AMS failure detection is very demanding with limited samples on account of high simulation and manufacturing cost. In this work, we combine a reversible network and a gating architecture to identify essential features from datasets and reduce feature dimension for fast failure detection. While reversible residual networks (RevNets) have been actively studied for its restoration ability from output to input without the loss of information, the gating network facilitates the RevNet to aim at effective dimension reduction. We incorporate the proposed reversible gating architecture into Bayesian optimization (BO) framework to reduce the dimensionality of BO embedding important features clarified by gating fusion weights so that the failure points can be efficiently located. Furthermore, we propose a conditional density estimation of important and non-important features to extract high-dimensional original input features from the low-dimension important features, improving the efficiency of the proposed methods. The improvements of our proposed approach on rare failure detection is demonstrated in AMS data under the high-dimensional process variations.
Year
DOI
Venue
2021
10.1109/DAC18074.2021.9586153
2021 58TH ACM/IEEE DESIGN AUTOMATION CONFERENCE (DAC)
Keywords
DocType
ISSN
Reversible neural network, dimension reduction, Bayesian optimization, gating architecture, failure detection
Conference
0738-100X
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Myung Seok Shim100.34
Hanbin Hu283.88
Peng Li31912152.85