  The higher speed, scalability and parallelism offered by ReRAM crossbar
arrays foster development of ReRAM-based next generation AI accelerators. At
the same time, sensitivity of ReRAM to temperature variations decreases
R_on/Roff ratio and negatively affects the achieved accuracy and reliability of
the hardware. Various works on temperature-aware optimization and remapping in
ReRAM crossbar arrays reported up to 58\% improvement in accuracy and
2.39$\times$ ReRAM lifetime enhancement. This paper classifies the challenges
caused by thermal heat, starting from constraints in ReRAM cells' dimensions
and characteristics to their placement in the architecture. In addition, it
reviews available solutions designed to mitigate the impact of these
challenges, including emerging temperature-resilient DNN training methods. Our
work also provides a summary of the techniques and their advantages and
limitations.
