  Emerging device-based Computing-in-memory (CiM) has been proved to be a
promising candidate for high-energy efficiency deep neural network (DNN)
computations. However, most emerging devices suffer uncertainty issues,
resulting in a difference between actual data stored and the weight value it is
designed to be. This leads to an accuracy drop from trained models to actually
deployed platforms. In this work, we offer a thorough analysis of the effect of
such uncertainties-induced changes in DNN models. To reduce the impact of
device uncertainties, we propose UAE, an uncertainty-aware Neural Architecture
Search scheme to identify a DNN model that is both accurate and robust against
device uncertainties.
