论文标题
RERAM横杆阵列中的热加热：挑战和解决方案
Thermal Heating in ReRAM Crossbar Arrays: Challenges and Solutions
论文作者
论文摘要
RERAM横杆阵列提供的较高速度，可扩展性和并行性促进了基于重新兰异的下一代AI加速器的开发。同时，重新兰异对温度变化的敏感性降低了R_ON/ROFF比率，并对硬件的准确性和可靠性产生负面影响。 RERAM横杆阵列中的各种温度感知优化和重新映射的工作报告据报道，准确性提高了58 \％，2.39 $ \ times $ reram liftime Eshancement。本文将热热造成的挑战归类，从重新兰细胞的尺寸和特征的约束开始到其在体系结构中的位置。此外，它还回顾了旨在减轻这些挑战的影响的可用解决方案，包括新兴温度耐热的DNN训练方法。我们的工作还提供了有关技术及其优势和局限性的摘要。
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.
