  The increasing amount of data processed on edge and the demand for reducing
the energy consumption for large neural network architectures have initiated
the transition from traditional von Neumann architectures towards in-memory
computing paradigms. Quantization is one of the methods to reduce power and
computation requirements for neural networks by limiting bit precision.
Resistive Random Access Memory (RRAM) devices are great candidates for
Quantized Neural Networks (QNN) implementations. As the number of possible
conductive states in RRAMs is limited, a certain level of quantization is
always considered when designing RRAM-based neural networks. In this work, we
provide a comprehensive analysis of state-of-the-art RRAM-based QNN
implementations, showing where RRAMs stand in terms of satisfying the criteria
of efficient QNN hardware. We cover hardware and device challenges related to
QNNs and show the main unsolved issues and possible future research directions.
