  Existing methods to recover model accuracy on analog-digital hardware in the
presence of quantization and analog noise include noise-injection training.
However, it can be slow in practice, incurring high computational costs, even
when starting from pretrained models. We introduce the Sensitivity-Aware
Finetuning (SAFT) approach that identifies noise sensitive layers in a model,
and uses the information to freeze specific layers for noise-injection
training. Our results show that SAFT achieves comparable accuracy to
noise-injection training and is 2x to 8x faster.
