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Article Dans Une Revue Journal of Electronic Testing: : Theory and Applications Année : 2020

On Using Approximate Computing to Build an Error Detection Scheme for Arithmetic Circuits

Résumé

Selecting the ideal trade-off between reliability improvement and cost (i.e., area, timing and power overhead) associated with a fault tolerant architecture generally requires an extensive Design Space Exploration. In this paper, we present a feasibility study that addresses the problem of selective hardening of arithmetic circuits by considering a duplication/comparison scheme as error detection architecture. Four different selective hardening methods have been investigated and compared: i) a full duplication scheme, ii) a reduced duplication scheme based on a structural susceptibility analysis, iii) a reduced duplication scheme based on the logical weight of the arithmetic circuit outputs and iv) a reduced duplication scheme that uses an approximate version of the arithmetic circuit. Experimental results performed on adder and multiplier case studies demonstrate the interest of using approximate structures in a duplication scheme since they provide much better error detection capability than other selective hardening methods with lower area and power overheads. Note that all experiments have been done without considering the area and power overhead due to the comparators. This may slightly biased the results from a quantitative point of view, although it does not jeopardize the main conclusion about the interest of using approximate structures as duplication scheme. Moreover, validations using a gate-level fault injection campaign have shown that approximate structures offer a better reliability level compared to the other considered duplication scenarios.
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Dates et versions

lirmm-02395626 , version 1 (05-12-2019)

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Bastien Deveautour, Arnaud Virazel, Patrick Girard, Valentin Gherman. On Using Approximate Computing to Build an Error Detection Scheme for Arithmetic Circuits. Journal of Electronic Testing: : Theory and Applications, 2020, 36, pp.33-46. ⟨10.1007/s10836-020-05858-5⟩. ⟨lirmm-02395626⟩
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