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Towards Enhancing Fault Tolerance in Neural Networks
[article]
2021
arXiv
pre-print
Deep Learning Accelerators are prone to faults which manifest in the form of errors in Neural Networks. Fault Tolerance in Neural Networks is crucial in real-time safety critical applications requiring computation for long durations. Neural Networks with high regularisation exhibit superior fault tolerance, however, at the cost of classification accuracy. In the view of difference in functionality, a Neural Network is modelled as two separate networks, i.e, the Feature Extractor with
arXiv:1907.03103v3
fatcat:rtywemwehjfxrm2civ6kri64qi