Applying bounded nonlinear functions (tanh, softsign, arctan) to DNN weights makes models tolerate random bit flips at BER 1e-5 with only a few points of accuracy loss.
Ft-clipact: Resilience analysis of deep neural networks and improving their fault tolerance using clipped activation,
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Enhancing Neural Network Robustness Against Fault Injection Through Non-linear Weight Transformations
Applying bounded nonlinear functions (tanh, softsign, arctan) to DNN weights makes models tolerate random bit flips at BER 1e-5 with only a few points of accuracy loss.