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Emerging device-based Computing-in-memory (CiM) has been proved to be apromising 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 isdesigned to be. This leads to an accuracy drop from trained models to actuallydeployed platforms. In this work, we offer a thorough analysis of the effect ofsuch uncertainties-induced changes in DNN models. To reduce the impact ofdevice uncertainties, we propose UAE, an uncertainty-aware Neural ArchitectureSearch scheme to identify a DNN model that is both accurate and robust againstdevice uncertainties.

Uncertainty Modeling of Emerging Device-based Computing-in-Memory Neural Accelerators with Application to Neural Architecture Search