  Over past years, the easy accessibility to the large scale datasets has
significantly shifted the paradigm for developing highly accurate prediction
models that are driven from Neural Network (NN). These models can be
potentially impacted by the radiation-induced transient faults that might lead
to the gradual downgrade of the long-running expected NN inference accelerator.
The crucial observation from our rigorous vulnerability assessment on the NN
inference accelerator demonstrates that the weights and activation functions
are unevenly susceptible to both single-event upset (SEU) and multi-bit upset
(MBU), especially in the first five layers of our selected convolution neural
network. In this paper, we present the relatively-accurate statistical models
to delineate the impact of both undertaken SEU and MBU across layers and per
each layer of the selected NN. These models can be used for evaluating the
error-resiliency magnitude of NN topology before adopting them in the
safety-critical applications.
