  Computing-in-memory with emerging non-volatile memory (nvCiM) is shown to be
a promising candidate for accelerating deep neural networks (DNNs) with high
energy efficiency. However, most non-volatile memory (NVM) devices suffer from
reliability issues, resulting in a difference between actual data involved in
the nvCiM computation and the weight value trained in the data center. Thus,
models actually deployed on nvCiM platforms achieve lower accuracy than their
counterparts trained on the conventional hardware (e.g., GPUs). In this
chapter, we first offer a brief introduction to the opportunities and
challenges of nvCiM DNN accelerators and then show the properties of different
types of NVM devices. We then introduce the general architecture of nvCiM DNN
accelerators. After that, we discuss the source of unreliability and how to
efficiently model their impact. Finally, we introduce representative works that
mitigate the impact of device variations.
