  The adoption of deep neural networks (DNNs) in safety-critical domains has
engendered serious reliability concerns. A prominent example is hardware
transient faults that are growing in frequency due to the progressive
technology scaling, and can lead to failures in DNNs.
  This work proposes Ranger, a low-cost fault corrector, which directly
rectifies the faulty output due to transient faults without re-computation.
DNNs are inherently resilient to benign faults (which will not cause output
corruption), but not to critical faults (which can result in erroneous output).
Ranger is an automated transformation to selectively restrict the value ranges
in DNNs, which reduces the large deviations caused by critical faults and
transforms them to benign faults that can be tolerated by the inherent
resilience of the DNNs. Our evaluation on 8 DNNs demonstrates Ranger
significantly increases the error resilience of the DNNs (by 3x to 50x), with
no loss in accuracy, and with negligible overheads.
