  Spiking neural networks have made breakthroughs in computer vision by lending
themselves to neuromorphic hardware. However, the neuromorphic hardware lacks
parallelism and hence, limits the throughput and hardware acceleration of SNNs
on edge devices. To address this problem, many systolic-array SNN accelerators
(systolicSNNs) have been proposed recently, but their reliability is still a
major concern. In this paper, we first extensively analyze the impact of
permanent faults on the SystolicSNNs. Then, we present a novel fault mitigation
method, i.e., fault-aware threshold voltage optimization in retraining
(FalVolt). FalVolt optimizes the threshold voltage for each layer in retraining
to achieve the classification accuracy close to the baseline in the presence of
faults. To demonstrate the effectiveness of our proposed mitigation, we
classify both static (i.e., MNIST) and neuromorphic datasets (i.e., N-MNIST and
DVS Gesture) on a 256x256 systolicSNN with stuck-at faults. We empirically show
that the classification accuracy of a systolicSNN drops significantly even at
extremely low fault rates (as low as 0.012\%). Our proposed FalVolt mitigation
method improves the performance of systolicSNNs by enabling them to operate at
fault rates of up to 60\%, with a negligible drop in classification accuracy
(as low as 0.1\%). Our results show that FalVolt is 2x faster compared to other
state-of-the-art techniques common in artificial neural networks (ANNs), such
as fault-aware pruning and retraining without threshold voltage optimization.
