A new methodology for noise sensor placement based on association rule mining

Yu Hsiang Hung, Sheng Hsin Fang, Hung-Ming Chen, Shen Min Chen, Chang Tzu Lin, Chia Hsin Lee

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

Abstract

Due to near-threshold computing nowadays, voltage emergency is threatening our design margins very seriously. Noise sensors are inserted in order to prevent various integrity issues from happening during runtime. In this work, we use a new technique based on association rule mining to plan and place noise sensors. This new methodology can consider the miss rate (the probability of any node occurring voltage emergency without any detection by placed sensors) and simultaneously minimize the number of sensors utilized. The results show that our approach is very effective in converging the miss rate to zero by the least number of sensors. Compared with the state-of-the-art, we can reduce the number of sensors by half in benchmarks while the miss rate is comparable or even smaller than the prior work.

Original languageEnglish
Title of host publicationGLSVLSI 2016 - Proceedings of the 2016 ACM Great Lakes Symposium on VLSI
PublisherAssociation for Computing Machinery
Pages81-86
Number of pages6
ISBN (Electronic)9781450342742
DOIs
StatePublished - 18 May 2016
Event26th ACM Great Lakes Symposium on VLSI, GLSVLSI 2016 - Boston, United States
Duration: 18 May 201620 May 2016

Publication series

NameProceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI
Volume18-20-May-2016

Conference

Conference26th ACM Great Lakes Symposium on VLSI, GLSVLSI 2016
CountryUnited States
CityBoston
Period18/05/1620/05/16

Keywords

  • Near threshold computing
  • Noise sensor placement
  • Power integrity

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  • Cite this

    Hung, Y. H., Fang, S. H., Chen, H-M., Chen, S. M., Lin, C. T., & Lee, C. H. (2016). A new methodology for noise sensor placement based on association rule mining. In GLSVLSI 2016 - Proceedings of the 2016 ACM Great Lakes Symposium on VLSI (pp. 81-86). (Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI; Vol. 18-20-May-2016). Association for Computing Machinery. https://doi.org/10.1145/2902961.2902973