Graph accelerators have emerged as a promising solution for processing large-scale sparse graphs, leveraging the in-situ compu-tation of ReRAM-based crossbars to maximize computational efficiency. However, existing designs suffer from memristor access overhead due to the large number of graph partitions. This leads to increased execution time, higher energy consumption, and re-duced circuit lifetime. This paper proposes a graph processing method that minimizes memristor write operations by identifying frequent subgraph patterns and assigning them to graph engines, referred to as static, allowing most subgraphs to be processed without a need for crossbar reconfiguration. Experimental results show speed up to 2.38x speedup and 7.23x energy savings com-pared to state-of-the-art accelerators. Furthermore, our method extends the circuit lifetime by 2x compared to state-of-the-art ReRAM graph accelerators.

翻译：图加速器已成为处理大规模稀疏图的一种有前景的解决方案，其利用基于ReRAM的交叉阵列原位计算来最大化计算效率。然而，现有设计因大量图分区而面临忆阻器访问开销，导致执行时间增加、能耗升高以及电路寿命缩短。本文提出一种图处理方法，通过识别频繁子图模式并将其分配给静态图引擎，从而最小化忆阻器写入操作，使得大多数子图无需交叉阵列重配置即可处理。实验结果表明，与最先进的加速器相比，该方法实现了高达2.38倍的加速和7.23倍的节能。此外，相较于现有ReRAM图加速器，我们的方法将电路寿命延长了2倍。