  Convolutional Neural Networks (CNNs) have become integral in safety-critical
applications, thus raising concerns about their fault tolerance. Conventional
hardware-dependent fault tolerance methods, such as Triple Modular Redundancy
(TMR), are computationally expensive, imposing a remarkable overhead on CNNs.
Whereas fault tolerance techniques can be applied either at the hardware level
or at the model levels, the latter provides more flexibility without
sacrificing generality. This paper introduces a model-level hardening approach
for CNNs by integrating error correction directly into the neural networks. The
approach is hardware-agnostic and does not require any changes to the
underlying accelerator device. Analyzing the vulnerability of parameters
enables the duplication of selective filters/neurons so that their output
channels are effectively corrected with an efficient and robust correction
layer. The proposed method demonstrates fault resilience nearly equivalent to
TMR-based correction but with significantly reduced overhead. Nevertheless,
there exists an inherent overhead to the baseline CNNs. To tackle this issue, a
cost-effective parameter vulnerability based pruning technique is proposed that
outperforms the conventional pruning method, yielding smaller networks with a
negligible accuracy loss. Remarkably, the hardened pruned CNNs perform up to
24\% faster than the hardened un-pruned ones.
