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Journal Articles ACM Computing Surveys Year : 2020

Binary Tree Classification of Rigid Error Detection and Correction Techniques

Abstract

Due to technology scaling and harsh environments, a wide range of fault tolerant techniques exists to deal with the error occurrences. Selecting a fault tolerant technique is not trivial, whereas more than the necessary overhead is usually inserted during the system design. To avoid over-designing, it is necessary to in-depth understand the available design options. However, an exhaustive listing is neither possible to create nor efficient to use due to its prohibited size. In this work, we present a top-down binary tree classification for error detection and correction techniques. At each split, the design space is clearly divided into two complementary parts using one single attribute, compared with existing classifications that use splits with multiple attributes. A leaf inherits all the attributes of its ancestors from the root to the leaf. A technique is decomposed into primitive components, each one belonging to a different leaf. The single attribute splits can be used to efficiently compare the techniques and to prune the incompatible parts of the design space during the design of a technique. This essential single attribute division of the design space is required for the improvement of the techniques and for novel contributions to the fault tolerance domain.
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Dates and versions

hal-02927439 , version 1 (29-01-2021)

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Angeliki Kritikakou, Rafail Psiakis, Francky Catthoor, Olivier Sentieys. Binary Tree Classification of Rigid Error Detection and Correction Techniques. ACM Computing Surveys, 2020, 53 (4), pp.1-38. ⟨10.1145/3397268⟩. ⟨hal-02927439⟩
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