  Deep Neural Network has proved its potential in various perception tasks and
hence become an appealing option for interpretation and data processing in
security sensitive systems. However, security-sensitive systems demand not only
high perception performance, but also design robustness under various
circumstances. Unlike prior works that study network robustness from software
level, we investigate from hardware perspective about the impact of Single
Event Upset (SEU) induced parameter perturbation (SIPP) on neural networks. We
systematically define the fault models of SEU and then provide the definition
of sensitivity to SIPP as the robustness measure for the network. We are then
able to analytically explore the weakness of a network and summarize the key
findings for the impact of SIPP on different types of bits in a floating point
parameter, layer-wise robustness within the same network and impact of network
depth. Based on those findings, we propose two remedy solutions to protect DNNs
from SIPPs, which can mitigate accuracy degradation from 28% to 0.27% for
ResNet with merely 0.24-bit SRAM area overhead per parameter.
