Doctoral Dissertations

Tao Wang

Abstract

"The relentless technology scaling has led to significantly reduced noise margin and complicated functionalities. As such, design time techniques per se are less likely to ensure power integrity, resulting in runtime voltage emergencies. To alleviate the issue, recently several works have shed light on the possibilities of dynamic noise management systems. Most of these works rely on on-chip noise sensors to accurately capture voltage emergencies. However, they all assume that the placement of the sensors is given. It remains an open problem in the literature how to optimally place a given number of noise sensors for best voltage emergency detection. The problem of noise sensor placement is defined at first along with a novel sensing quality metric (SQM) to be maximized.

The threshold voltage for noise sensors to report emergencies serves as a critical tuning knob between the system failure rate and false alarms. The problem of minimizing the system alarm rate subject to a given system failure rate constraint is formulated. It is further shown that with the help of IDDQ measurements during testing which reveal process variation information, it is possible and efficient to compute a per-chip optimal threshold voltage threshold.

In the third chapter, a novel framework to predict the resonance frequency using existing on-chip noise sensors, based on the theory of 1-bit compressed sensing is proposed. The proposed framework can help to achieve the resonance frequency of individual chips so as to effectively avoid resonance noise at runtime"--Abstract, page iii.

Advisor(s)

Shi, Yiyu

Committee Member(s)

Beetner, Daryl G.
Fan, Jun, 1971-
Choi, Minsu
Cheng, Maggie Xiaoyan

Department(s)

Electrical and Computer Engineering

Degree Name

Ph. D. in Computer Engineering

Publisher

Missouri University of Science and Technology

Publication Date

Fall 2015

Journal article titles appearing in thesis/dissertation

  • Eagle-eye: A near-optimal statistical framework for noise sensor placement
  • On the optimal threshold voltage computation of on-chip noise sensors
  • 1-bit compressed sensing based framework for built-in resonance frequency prediction using on-chip noise sensors

Pagination

x, 69 pages

Note about bibliography

Includes bibliographic references.

Rights

© 2015 Tao Wang, All rights reserved.

Document Type

Dissertation - Open Access

File Type

text

Language

English

Subject Headings

Signal processing
Sensor networks
Electric power system stability
Electric power systems -- Control -- Mathematical models
Iddq testing

Thesis Number

T 10839

Electronic OCLC #

936209499

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