  As artificial neural networks become increasingly integrated into
safety-critical systems such as autonomous vehicles, devices for medical
diagnosis, and industrial automation, ensuring their reliability in the face of
random hardware faults becomes paramount. This paper introduces SpikingJET, a
novel fault injector designed specifically for fully connected and
convolutional Spiking Neural Networks (SNNs). Our work underscores the critical
need to evaluate the resilience of SNNs to hardware faults, considering their
growing prominence in real-world applications. SpikingJET provides a
comprehensive platform for assessing the resilience of SNNs by inducing errors
and injecting faults into critical components such as synaptic weights, neuron
model parameters, internal states, and activation functions. This paper
demonstrates the effectiveness of Spiking-JET through extensive software-level
experiments on various SNN architectures, revealing insights into their
vulnerability and resilience to hardware faults. Moreover, highlighting the
importance of fault resilience in SNNs contributes to the ongoing effort to
enhance the reliability and safety of Neural Network (NN)-powered systems in
diverse domains.
