Leveraging Recurrent Patterns in Graph Accelerators
For hardware designers of graph accelerators, this work addresses the bottleneck of memristor write overhead with a pattern-based approach, yielding substantial improvements in speed, energy, and lifetime.
This paper tackles the memristor access overhead in ReRAM-based graph accelerators by identifying frequent subgraph patterns and assigning them to static graph engines, reducing crossbar reconfiguration. The method achieves up to 2.38x speedup, 7.23x energy savings, and 2x circuit lifetime extension over state-of-the-art accelerators.
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.