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.

翻译：ReRAM交叉阵列提供的高速度、可扩展性和并行性促进了基于ReRAM的下一代AI加速器的发展。与此同时，ReRAM对温度变化的敏感性降低了R_on/Roff比，并对硬件的准确性和可靠性产生负面影响。多项关于ReRAM交叉阵列中温度感知优化和重映射的研究报告指出，准确率可提升高达58%，ReRAM寿命延长至2.39倍。本文对由热加热引起的挑战进行了分类，从ReRAM单元尺寸和特性的限制到其在架构中的布局。此外，本文综述了旨在缓解这些挑战影响的现有解决方案，包括新兴的耐温度深度神经网络训练方法。我们的工作还总结了这些技术及其优势与局限性。