The high speed, scalability, and parallelism offered by ReRAM crossbar arrays foster the development of ReRAM-based next-generation AI accelerators. At the same time, the sensitivity of ReRAM to temperature variations decreases $\text{R}_{ON}/\text{R}_{OFF}$ 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 the available solutions designed to mitigate the impact of these challenges, including emerging temperature-resilient Deep Neural Network (DNN) training methods. Our work also provides a summary of the techniques and their advantages and limitations.

中文翻译：

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ReRAM 交叉阵列中的热加热：挑战和解决方案

ReRAM 交叉阵列提供的高速、可扩展性和并行性促进了基于 ReRAM 的下一代人工智能加速器的开发。同时，ReRAM对温度变化的敏感性降低 $\text{R}_{ON}/\text{R}_{OFF}$比率并对硬件所实现的精度和可靠性产生负面影响。ReRAM 交叉阵列中温度感知优化和重新映射的各种工作报告称，准确度和重新映射的精度提高了 58% $2.39\次$ReRAM 寿命延长。本文对热引起的挑战进行了分类，从 ReRAM 单元尺寸和特性的限制到它们在架构中的放置。此外，它还回顾了旨在减轻这些挑战影响的可用解决方案，包括新兴的温​​度弹性深度神经网络 (DNN) 训练方法。我们的工作还总结了这些技术及其优点和局限性。