In-place zero-space memory protection for CNN

Hui Guan, Lin Ning, Zhen Lin, Xipeng Shen, Huiyang Zhou, Seung Hwan Lim

Research output: Contribution to journalConference articlepeer-review

17 Scopus citations

Abstract

Convolutional Neural Networks (CNN) are being actively explored for safety-critical applications such as autonomous vehicles and aerospace, where it is essential to ensure the reliability of inference results in the presence of possible memory faults. Traditional methods such as error correction codes (ECC) and Triple Modular Redundancy (TMR) are CNN-oblivious and incur substantial memory overhead and energy cost. This paper introduces in-place zero-space ECC assisted with a new training scheme weight distribution-oriented training. The new method provides the first known zero space cost memory protection for CNNs without compromising the reliability offered by traditional ECC.

Original languageEnglish
JournalAdvances in Neural Information Processing Systems
Volume32
StatePublished - 2019
Event33rd Annual Conference on Neural Information Processing Systems, NeurIPS 2019 - Vancouver, Canada
Duration: Dec 8 2019Dec 14 2019

Funding

Acknowledgement We would like to thank the anonymous reviews for their helpful feedbacks. This material is based upon work supported by the National Science Foundation (NSF) under Grant No. CCF-1525609, CCF-1703487, CCF-1717550, and CCF-1908406. Any opinions, comments, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of NSF. This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). We would like to thank the anonymous reviews for their helpful feedbacks. This material is based upon work supported by the National Science Foundation (NSF) under Grant No. CCF-1525609, CCF-1703487, CCF-1717550, and CCF-1908406. Any opinions, comments, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of NSF. This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

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