  The advent of deep learning has resulted in a number of applications which
have transformed the landscape of the research area in which it has been
applied. However, with an increase in popularity, the complexity of classical
deep neural networks has increased over the years. As a result, this has leads
to considerable problems during deployment on devices with space and time
constraints. In this work, we perform a review of the present advancements in
non-volatile memory and how the use of resistive RAM memory, particularly
memristors, can help to progress the state of research in deep learning. In
other words, we wish to present an ideology that advances in the field of
memristive technology can greatly influence and impact deep learning inference
on edge devices.
