SoftSNN: Low-Cost Fault Tolerance for Spiking Neural Network Accelerators under Soft Errors. (arXiv:2203.05523v1 [cs.AR])

Specialized hardware accelerators have been designed and employed to maximize
the performance efficiency of Spiking Neural Networks (SNNs). However, such
accelerators are vulnerable to transient faults (i.e., soft errors), which
occur due to high-energy particle strikes, and manifest as bit flips at the
hardware layer. These errors can change the weight values and neuron operations
in the compute engine of SNN accelerators, thereby leading to incorrect outputs
and accuracy degradation. However, the impact of soft errors in the compute
engine …

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