  Low-precision weights and activations in deep neural networks (DNNs)
outperform their full-precision counterparts in terms of hardware efficiency.
When implemented with low-precision operations, specifically in the extreme
case where network parameters are binarized (i.e. BNNs), the two most
frequently mentioned benefits of quantization are reduced memory consumption
and a faster inference process. In this paper, we introduce a third advantage
of very low-precision neural networks: improved fault-tolerance attribute. We
investigate the impact of memory faults on state-of-the-art binary neural
networks (BNNs) through comprehensive analysis. Despite the inclusion of
floating-point parameters in BNN architectures to improve accuracy, our
findings reveal that BNNs are highly sensitive to deviations in these
parameters caused by memory faults. In light of this crucial finding, we
propose a technique to improve BNN dependability by restricting the range of
float parameters through a novel deliberately uniform quantization. The
introduced quantization technique results in a reduction in the proportion of
floating-point parameters utilized in the BNN, without incurring any additional
computational overheads during the inference stage. The extensive experimental
fault simulation on the proposed BNN architecture (i.e. ZOBNN) reveal a
remarkable 5X enhancement in robustness compared to conventional floating-point
DNN. Notably, this improvement is achieved without incurring any computational
overhead. Crucially, this enhancement comes without computational overhead.
\ToolName~excels in critical edge applications characterized by limited
computational resources, prioritizing both dependability and real-time
performance.
