Resistive Random Access Memory (ReRAM) has emerged as a promising platform
for deep neural networks (DNNs) due to its support for parallel in-situ
matrix-vector multiplication. However, hardware failures, such as
stuck-at-fault defects, can result in significant prediction errors during
model inference. While additional crossbars can be used to address these
failures, they come with storage overhead and are not efficient in terms of
space, energy, and cost. In this paper, we propose a fault protection mechanism
that incurs zero space cost. Our approach includes: 1) differentiable structure
pruning of rows and columns to reduce model redundancy, 2) weight duplication
and voting for robust output, and 3) embedding duplicated most significant bits
(MSBs) into the model weight. We evaluate our method on nine tasks of the GLUE
benchmark with the BERT model, and experimental results prove its
effectiveness.

本文提出了一种零空间成本的容错机制，通过可微结构修剪、权重复制和投票以及嵌入最显著位（MSBs）到模型权重中，有效减少了硬件故障对模型预测的影响，并通过在 GLUE 基准的九个任务上使用 BERT 模型进行实验验证了该方法的有效性。