Enhancing Neural Network Reliability: Insights From Hardware/Software Collaboration With Neuron Vulnerability Quantization

Ensuring the reliability of deep neural networks (DNNs) is paramount in safety-critical applications. Although introducing supplementary fault-tolerant mechanisms can augment the reliability of DNNs, an efficiency tradeoff may be introduced. This study reveals the inherent fault tolerance of neural...

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Vydané v:	IEEE transactions on computers Ročník 73; číslo 8; s. 1953 - 1966
Hlavní autori:	Wang, Jing, Zhu, Jinbin, Fu, Xin, Zang, Di, Li, Keyao, Zhang, Weigong
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.08.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Artificial neural networks Collaboration Computer network reliability Computing time Error correcting codes Error correction Fault tolerance Fault tolerant systems Hardware memory protection Network reliability neural network Neural networks neuron vulnerability factor Neurons Performance degradation Reliability Safety critical soft error Soft errors Software Software reliability Structural reliability
ISSN:	0018-9340, 1557-9956
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