  Large-scale integration of emerging nanoscale non-volatile memory devices,
e.g. resistive random-access memory (RRAM), can enable a new generation of
neuromorphic computers that can solve a wide range of machine learning
problems. Such hybrid CMOS-RRAM neuromorphic architectures will result in
several orders of magnitude reduction in energy consumption at a very small
form factor, and herald autonomous learning machines capable of self-adapting
to their environment. However, the progress in this area has been impeded from
the realization that the actual memory devices fall well short of their
expected behavior. In this work, we discuss the challenges associated with
these memory devices and their use in neuromorphic computing circuits, and
propose pathways to overcome these limitations by introducing 'dendritic
learning'.
