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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Bibliographic Details
Published in:	IEEE transactions on computers Vol. 73; no. 8; pp. 1953 - 1966
Main Authors:	Wang, Jing, Zhu, Jinbin, Fu, Xin, Zang, Di, Li, Keyao, Zhang, Weigong
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.08.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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
Online Access:	Get full text
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