Towards verification-aware knowledge distillation for neural-network controlled systems: Invited paper

Jiameng Fan, Chao Huang, Wenchao Li, Xin Chen, Qi Zhu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Scopus citations

Abstract

Neural networks are widely used in many applications ranging from classification to control. While these networks are composed of simple arithmetic operations, they are challenging to formally verify for properties such as reachability due to the presence of nonlinear activation functions. In this paper, we make the observation that Lipschitz continuity of a neural network not only can play a major role in the construction of reachable sets for neural-network controlled systems but also can be systematically controlled during training of the neural network. We build on this observation to develop a novel verification-aware knowledge distillation framework that transfers the knowledge of a trained network to a new and easier-to-verify network. Experimental results show that our method can substantially improve reachability analysis of neural-network controlled systems for several state-of-the-art tools.

Original languageEnglish (US)
Title of host publication2019 IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2019 - Digest of Technical Papers
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728123509
DOIs
StatePublished - Nov 2019
Event38th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2019 - Westin Westminster, United States
Duration: Nov 4 2019Nov 7 2019

Publication series

NameIEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
Volume2019-November
ISSN (Print)1092-3152

Conference

Conference38th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2019
Country/TerritoryUnited States
CityWestin Westminster
Period11/4/1911/7/19

Funding

ACKNOWLEDGMENT This work was supported in part by the DARPA BRASS program under agreement number FA8750-16-C-0043, NSF under award number 1834701, 1834324, 1839511, 1724341 and 1646497, and by the Air Force Research Laboratory (AFRL).

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

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