  Spiking neural networks (SNNs) have shown a potential for having low energy
with unsupervised learning capabilities due to their biologically-inspired
computation. However, they may suffer from accuracy degradation if their
processing is performed under the presence of hardware-induced faults in
memories, which can come from manufacturing defects or voltage-induced
approximation errors. Since recent works still focus on the fault-modeling and
random fault injection in SNNs, the impact of memory faults in SNN hardware
architectures on accuracy and the respective fault-mitigation techniques are
not thoroughly explored. Toward this, we propose ReSpawn, a novel framework for
mitigating the negative impacts of faults in both the off-chip and on-chip
memories for resilient and energy-efficient SNNs. The key mechanisms of ReSpawn
are: (1) analyzing the fault tolerance of SNNs; and (2) improving the SNN fault
tolerance through (a) fault-aware mapping (FAM) in memories, and (b)
fault-aware training-and-mapping (FATM). If the training dataset is not fully
available, FAM is employed through efficient bit-shuffling techniques that
place the significant bits on the non-faulty memory cells and the insignificant
bits on the faulty ones, while minimizing the memory access energy. Meanwhile,
if the training dataset is fully available, FATM is employed by considering the
faulty memory cells in the data mapping and training processes. The
experimental results show that, compared to the baseline SNN without
fault-mitigation techniques, ReSpawn with a fault-aware mapping scheme improves
the accuracy by up to 70% for a network with 900 neurons without retraining.
