Ares: A framework for quantifying the resilience of deep neural networks

Proceedings - Design Automation Conference

Volumn Part F137710, Issue , 2018, Pages

  Reagen, Brandon ^a   Gupta, Udit ^a   Pentecost, Lillian ^a   Whatmough, Paul ^a   Lee, Sae Kyu ^a   Mulholland, Niamh ^a   Brooks, David ^a   Wei, Gu Yeon ^a  

^a HARVARD UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER AIDED DESIGN; FAULT TOLERANCE; HARDWARE;

ARCHITECTURE COMMUNITY; EMPIRICAL STUDIES; FAULT INJECTION; FAULT RATES; LIGHT WEIGHT; MODEL ACCURACY; ORDERS OF MAGNITUDE;

DEEP NEURAL NETWORKS;

EID: 85053668751     PISSN: 0738100X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/3195970.3195997     Document Type: Conference Paper

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