Error Mitigation Using Approximate Logic Circuits: A Comparison of Probabilistic and Evolutionary Approaches

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Antonio J. Sanchez-Clemente and Luis Entrena and 
                 Radek Hrbacek and Lukas Sekanina",
  title =        "Error Mitigation Using Approximate Logic Circuits: A
                 Comparison of Probabilistic and Evolutionary
  journal =      "IEEE Transactions on Reliability",
  year =         "2016",
  volume =       "65",
  number =       "4",
  pages =        "1871--1883",
  month =        dec,
  keywords =     "genetic algorithms, genetic programming, cartesian
                 genetic programming, Approximate logic circuit, error
                 mitigation, evolutionary computing, single-event
                 transient (SET), single-event upset (SEU)",
  ISSN =         "0018-9529",
  URL =          "",
  DOI =          "doi:10.1109/TR.2016.2604918",
  size =         "13 pages",
  abstract =     "Technology scaling poses an increasing challenge to
                 the reliability of digital circuits. Hardware
                 redundancy solutions, such as triple modular redundancy
                 (TMR), produce very high area overhead, so partial
                 redundancy is often used to reduce the overheads.
                 Approximate logic circuits provide a general framework
                 for optimized mitigation of errors arising from a broad
                 class of failure mechanisms, including transient,
                 intermittent, and permanent failures. However,
                 generating an optimal redundant logic circuit that is
                 able to mask the faults with the highest probability
                 while minimizing the area overheads is a challenging
                 problem. In this study, we propose and compare two new
                 approaches to generate approximate logic circuits to be
                 used in a TMR schema. The probabilistic approach
                 approximates a circuit in a greedy manner based on a
                 probabilistic estimation of the error. The evolutionary
                 approach can provide radically different solutions that
                 are hard to reach by other methods. By combining these
                 two approaches, the solution space can be explored in
                 depth. Experimental results demonstrate that the
                 evolutionary approach can produce better solutions, but
                 the probabilistic approach is close. On the other hand,
                 these approaches provide much better scalability than
                 other existing partial redundancy techniques.",
  notes =        "Also known as \cite{7579598}",

Genetic Programming entries for Antonio Jose Sanchez-Clemente Luis Alfonso Entrena Arrontes Radek Hrbacek Lukas Sekanina