Revisiting Vulnerability Analysis in Modern Microprocessors

Michail Maniatakos, Maria Michael, Chandra Tirumurti, Yiorgos Makris

Research output: Contribution to journalArticlepeer-review

Abstract

The notion of Architectural Vulnerability Factor (AVF) has been extensively used to evaluate various aspects of design robustness. While AVF has been a very popular way of assessing element resiliency, its calculation requires rigorous and extremely time-consuming experiments. Furthermore, recent radiation studies in 90 nm and 65 nm technology nodes demonstrate that up to 55 percent of Single Event Upsets (SEUs) result in Multiple Bit Upsets (MBUs), and thus the Single Bit Flip (SBF) model employed in computing AVF needs to be reassessed. In this paper, we present a method for calculating the vulnerability of modern microprocessors -using Statistical Fault Injection (SFI)- several orders of magnitude faster than traditional SFI techniques, while also using more realistic fault models which reflect the existence of MBUs. Our method partitions the design into various hierarchical levels and systematically performs incremental fault injections to generate vulnerability estimates. The presented method has been applied on an Intel microprocessor and an Alpha 21264 design, accelerating fault injection by 15×, on average, and reducing computational cost for investigating the effect of MBUs. Extensive experiments, focusing on the effect of MBUs in modern microprocessors, corroborate that the SBF model employed by current vulnerability estimation tools is not sufficient to accurately capture the increasing effect of MBUs in contemporary processes.

Original languageEnglish (US)
Article number6967809
Pages (from-to)2664-2674
Number of pages11
JournalIEEE Transactions on Computers
Volume64
Issue number9
DOIs
StatePublished - Sep 1 2015

Keywords

  • AVF
  • microprocessor vulnerability
  • multiple bit upset
  • statistical fault injection

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Hardware and Architecture
  • Computational Theory and Mathematics

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