NOFIS: Normalizing Flow for Rare Circuit Failure Analysis
Name
3649329.3658459.pdf
Size
3.53 MB
Format
Adobe PDF
Checksum (MD5)
11e7eb54e4ce090ae6ca5e87856f4d00
Author(s)
Gao, Zhengqi
Zhang, Dinghuai
Daniel, Luca
Boning, Duane
Date Issued
June 23, 2024
Publisher
ACM|61st ACM/IEEE Design Automation Conference
Citation
Accurate estimation of rare failure occurrence probability is crucial for ensuring the proper and reliable functioning of integrated circuits (ICs). Conventional Monte Carlo methods are inefficient, demanding an exorbitant number of samples to achieve reliable estimates. Inspired by the exact sampling capabilities of normalizing flows, we revisit this problem and propose normalizing flow assisted importance sampling, termed NOFIS. NOFIS first learns a sequence of proposal distributions associated with predefined nested subset events by minimizing KL divergence losses. Next, it estimates the rare event probability by utilizing importance sampling in conjunction with the last proposal. The efficacy of our NOFIS method is substantiated through comprehensive qualitative visualizations, affirming the optimality of the learned proposal distribution, as well as 10 quantitative experiments, which highlight NOFIS's superior accuracy over baseline approaches.
Version
Final published version
Description
DAC ’24, June 23–27, 2024, San Francisco, CA, USA
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1145/3649329.3658459
