Next Article in Journal
An Efficient Radio Access Control Mechanism for Wireless Network-On-Chip Architectures
Previous Article in Journal
Acknowledgement to Reviewers of the Journal of Low Power Electronics and Applications in 2014
Article Menu

Export Article

Open AccessArticle
J. Low Power Electron. Appl. 2015, 5(1), 3-37;

Stochastically Estimating Modular Criticality in Large-Scale Logic Circuits Using Sparsity Regularization and Compressive Sensing

Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL 32816, USA
Author to whom correspondence should be addressed.
Academic Editor: Alexander Fish
Received: 29 October 2014 / Revised: 21 January 2015 / Accepted: 3 March 2015 / Published: 13 March 2015
Full-Text   |   PDF [577 KB, uploaded 13 March 2015]   |  


This paper considers the problem of how to efficiently measure a large and complex information field with optimally few observations. Specifically, we investigate how to stochastically estimate modular criticality values in a large-scale digital circuit with a very limited number of measurements in order to minimize the total measurement efforts and time. We prove that, through sparsity-promoting transform domain regularization and by strategically integrating compressive sensing with Bayesian learning, more than 98% of the overall measurement accuracy can be achieved with fewer than 10% of measurements as required in a conventional approach that uses exhaustive measurements. Furthermore, we illustrate that the obtained criticality results can be utilized to selectively fortify large-scale digital circuits for operation with narrow voltage headrooms and in the presence of soft-errors rising at near threshold voltage levels, without excessive hardware overheads. Our numerical simulation results have shown that, by optimally allocating only 10% circuit redundancy, for some large-scale benchmark circuits, we can achieve more than a three-times reduction in its overall error probability, whereas if randomly distributing such 10% hardware resource, less than 2% improvements in the target circuit’s overall robustness will be observed. Finally, we conjecture that our proposed approach can be readily applied to estimate other essential properties of digital circuits that are critical to designing and analyzing them, such as the observability measure in reliability analysis and the path delay estimation in stochastic timing analysis. The only key requirement of our proposed methodology is that these global information fields exhibit a certain degree of smoothness, which is universally true for almost any physical phenomenon. View Full-Text
Keywords: criticality analysis; VLSI; logic circuit criticality analysis; VLSI; logic circuit

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Alawad, M.; DeMara, R.F.; Lin, M. Stochastically Estimating Modular Criticality in Large-Scale Logic Circuits Using Sparsity Regularization and Compressive Sensing. J. Low Power Electron. Appl. 2015, 5, 3-37.

Show more citation formats Show less citations formats

Article Metrics

Article Access Statistics



[Return to top]
J. Low Power Electron. Appl. EISSN 2079-9268 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top