  Deep Neural Network (DNN) accelerators are extensively used to improve the
computational efficiency of DNNs, but are prone to faults through Single-Event
Upsets (SEUs). In this work, we present an in-depth analysis of the impact of
SEUs on a Systolic Array (SA) based DNN accelerator. A fault injection campaign
is performed through a Register-Transfer Level (RTL) based simulation
environment to improve the observability of each hardware block, including the
SA itself as well as the post-processing pipeline. From this analysis, we
present the sensitivity, independent of a DNN model architecture, for various
flip-flop groups both in terms of fault propagation probability and fault
magnitude. This allows us to draw detailed conclusions and determine optimal
mitigation strategies.
