dc.creator | Chetia, Jugantor | |
dc.date.accessioned | 2020-08-21T20:57:52Z | |
dc.date.available | 2012-02-03 | |
dc.date.issued | 2012-02-03 | |
dc.identifier.uri | https://etd.library.vanderbilt.edu/etd-01252012-174557 | |
dc.identifier.uri | http://hdl.handle.net/1803/10484 | |
dc.description.abstract | Soft errors induced by radiation particles are increasingly becoming a source of concern for
reliable design of VLSI systems. An important parameter to quantify the soft error response of a
system is Architectural Vulnerability Factor (AVF), which is the probability that a fault in a part
of the system will result in an error. One of the most common techniques to estimate AVF of a
system is by using statistical fault injection (SFI). Traditional SFI techniques can be
computationally inefficient with increasing design complexity. In this work, a novel technique
has been developed to enable computationally efficient AVF estimation using enhanced node
observability. Possible target applications for this technique have been identified with results
showing orders of magnitude improvement over traditional SFI techniques. | |
dc.format.mimetype | application/pdf | |
dc.subject | AVF | |
dc.subject | statistical fault injection | |
dc.subject | circuit partitioning | |
dc.title | An efficient AVF estimation technique using circuit partitioning | |
dc.type | thesis | |
dc.type.material | text | |
thesis.degree.name | MS | |
thesis.degree.level | thesis | |
thesis.degree.discipline | Electrical Engineering | |
thesis.degree.grantor | Vanderbilt University | |
local.embargo.terms | 2012-02-03 | |
local.embargo.lift | 2012-02-03 | |
dc.contributor.committeeChair | Lloyd W. Massengill | |
dc.contributor.committeeChair | Bharat L. Bhuva | |