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FAT: Training Neural Networks for Reliable Inference Under Hardware
  Faults

FAT: Training Neural Networks for Reliable Inference Under Hardware Faults

11 November 2020
Ussama Zahid
Giulio Gambardella
Nicholas J. Fraser
Michaela Blott
K. Vissers
ArXivPDFHTML

Papers citing "FAT: Training Neural Networks for Reliable Inference Under Hardware Faults"

5 / 5 papers shown
Title
Enhancing Fault Resilience of QNNs by Selective Neuron Splitting
Enhancing Fault Resilience of QNNs by Selective Neuron Splitting
Mohammad Hasan Ahmadilivani
Mahdi Taheri
J. Raik
Masoud Daneshtalab
M. Jenihhin
AAML
21
9
0
16 Jun 2023
FAQ: Mitigating the Impact of Faults in the Weight Memory of DNN
  Accelerators through Fault-Aware Quantization
FAQ: Mitigating the Impact of Faults in the Weight Memory of DNN Accelerators through Fault-Aware Quantization
Muhammad Abdullah Hanif
Muhammad Shafique
AAML
31
2
0
21 May 2023
A Systematic Literature Review on Hardware Reliability Assessment
  Methods for Deep Neural Networks
A Systematic Literature Review on Hardware Reliability Assessment Methods for Deep Neural Networks
Mohammad Hasan Ahmadilivani
Mahdi Taheri
J. Raik
Masoud Daneshtalab
M. Jenihhin
35
25
0
09 May 2023
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image
  Segmentation
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
Vijay Badrinarayanan
Alex Kendall
R. Cipolla
SSeg
446
15,639
0
02 Nov 2015
Improving neural networks by preventing co-adaptation of feature
  detectors
Improving neural networks by preventing co-adaptation of feature detectors
Geoffrey E. Hinton
Nitish Srivastava
A. Krizhevsky
Ilya Sutskever
Ruslan Salakhutdinov
VLM
266
7,634
0
03 Jul 2012
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