  We present a highly compact run-time monitoring approach for deep computer
vision networks that extracts selected knowledge from only a few (down to
merely two) hidden layers, yet can efficiently detect silent data corruption
originating from both hardware memory and input faults. Building on the insight
that critical faults typically manifest as peak or bulk shifts in the
activation distribution of the affected network layers, we use strategically
placed quantile markers to make accurate estimates about the anomaly of the
current inference as a whole. Importantly, the detector component itself is
kept algorithmically transparent to render the categorization of regular and
abnormal behavior interpretable to a human. Our technique achieves up to ~96%
precision and ~98% recall of detection. Compared to state-of-the-art anomaly
detection techniques, this approach requires minimal compute overhead (as
little as 0.3% with respect to non-supervised inference time) and contributes
to the explainability of the model.
