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Machine Learning for Noise Sensor Placement and Full-Chip Voltage Emergency Detection

Publication ,  Journal Article
Liu, X; Sun, S; Li, X; Qian, H; Zhou, P
Published in: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
March 1, 2017

Power supply fluctuation can be potential threat to the correct operations of processors, in the form of voltage emergency that happens when supply voltage drops below a certain threshold. Noise sensors (with either analog or digital outputs) can be placed in the nonfunction area of processors to detect voltage emergencies by monitoring the runtime voltage fluctuations. Our work addresses two important problems related to building a sensor-based voltage emergency detection system: 1) offline sensor placement, i.e., where to place the noise sensors so that the number and locations of sensors are optimized in order to strike a balance between design cost and chip reliability and 2) online voltage emergency detection, i.e., how to use these placed sensors to detect voltage emergencies in the hotspot locations. In this paper, we propose integrated solutions to these two problems, respectively, for analog and digital (more specifically, binary) sensor outputs, by exploiting the voltage correlation between the sensor candidate locations and the hotspot locations. For the analog case, we use the Group Lasso and an ordinary least squares approach; for the binary case, we integrate the Group Lasso and the SVM approach. Experimental results show that, our approach can achieve 2.3X-2.7X better voltage emergency detection results on average for analog outputs when compared to the state-of-the-art work; and for the binary case, on average our methodology can achieve up to 21% improvement in prediction accuracy compared to an approach called max-probability-no-prediction.

Duke Scholars

Published In

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems

DOI

ISSN

0278-0070

Publication Date

March 1, 2017

Volume

36

Issue

3

Start / End Page

421 / 434

Related Subject Headings

  • Computer Hardware & Architecture
  • 4607 Graphics, augmented reality and games
  • 4009 Electronics, sensors and digital hardware
  • 1006 Computer Hardware
  • 0906 Electrical and Electronic Engineering
 

Citation

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ICMJE
MLA
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Liu, X., Sun, S., Li, X., Qian, H., & Zhou, P. (2017). Machine Learning for Noise Sensor Placement and Full-Chip Voltage Emergency Detection. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 36(3), 421–434. https://doi.org/10.1109/TCAD.2016.2611502
Liu, X., S. Sun, X. Li, H. Qian, and P. Zhou. “Machine Learning for Noise Sensor Placement and Full-Chip Voltage Emergency Detection.” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 36, no. 3 (March 1, 2017): 421–34. https://doi.org/10.1109/TCAD.2016.2611502.
Liu X, Sun S, Li X, Qian H, Zhou P. Machine Learning for Noise Sensor Placement and Full-Chip Voltage Emergency Detection. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 2017 Mar 1;36(3):421–34.
Liu, X., et al. “Machine Learning for Noise Sensor Placement and Full-Chip Voltage Emergency Detection.” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 36, no. 3, Mar. 2017, pp. 421–34. Scopus, doi:10.1109/TCAD.2016.2611502.
Liu X, Sun S, Li X, Qian H, Zhou P. Machine Learning for Noise Sensor Placement and Full-Chip Voltage Emergency Detection. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 2017 Mar 1;36(3):421–434.

Published In

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems

DOI

ISSN

0278-0070

Publication Date

March 1, 2017

Volume

36

Issue

3

Start / End Page

421 / 434

Related Subject Headings

  • Computer Hardware & Architecture
  • 4607 Graphics, augmented reality and games
  • 4009 Electronics, sensors and digital hardware
  • 1006 Computer Hardware
  • 0906 Electrical and Electronic Engineering