Selectively Breaking Data Dependences to Improve the Utilization of Idle Cycles in Algorithm Level Re-Computing Data Paths

Kaijie Wu, Ramesh Karri

Research output: Contribution to journalArticle


Although algorithm level re-computing techniques can trade-off the fault detection capability vs. time overhead of a Concurrent Error Detection (CED) scheme, they result in 100% time overhead when the strongest CED capability is achieved. Using the idle cycles in the data path to do the re-computation can reduce this time overhead. However, dependences between operations prevent the re-computation from fully utilizing the idle cycles. Deliberately breaking some of these data dependences can further reduce the time overhead associated with algorithm level re-computing. According to the experimental results the proposed technique, it brings time overhead down to 0-60% while the associated hardware overhead is from 12% to 50% depending on the design size.

Original languageEnglish (US)
Pages (from-to)501-511
Number of pages11
JournalIEEE Transactions on Reliability
Issue number4
StatePublished - Dec 1 2003



  • Concurrent error detection
  • Radiation hardening
  • Very deep sub-micron device

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Electrical and Electronic Engineering

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