Zusammenfassung The market potential of safety critical products using AI is very attractive and Deep Learning Neural Networks (NN) have proven strengths to provide important functionality. This paper describes some of the challenges in arguing safety of systems using Deep Learning NN, especially functional improvement in context of SOTIF (Safety of the Intended Functionality) or other approaches to provide the safety case. An architecture and independence controller is proposed which can be used beneficially to reduce residual risk of functional insufficiencies for Deep Learning NN based systems.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Architecture and independence controller for deep learning in safety critical applications


    Contributors:


    Publication date :

    2019-01-01


    Size :

    12 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    German





    Monoprocessed computer architecture for safety critical applications

    de Almeida, J. R. / Fonseca, J. A. | British Library Conference Proceedings | 2002


    DACAPO: A Distributed Computer Architecture for Safety-Critical Control Applications

    Rostamzadeh, B. / Lonn, H. / Snedsbol, R. et al. | British Library Conference Proceedings | 1995


    Experimental Allocation of Safety-Critical Applications on Reconfigurable Multi-Core Architecture

    Sutter, Louis / Khamvilai, Thanakorn / Monmousseau, Philippe et al. | IEEE | 2018


    A Fault-Tolerant Processor Core Architecture for Safety-Critical Automotive Applications

    Touloupis, E. / Flint, J. A. / Chouliaras, V. A. et al. | British Library Conference Proceedings | 2005