GraphS: A Graph Processing Accelerator Leveraging SOT-MRAM
Shaahin Angizi, Jiao Sun, Wei Zhang, Deliang Fan · 2019
In this work, we present GraphS architecture, which transforms current Spin Orbit Torque Magnetic Random Access Memory (SOT-MRAM) to massively parallel computational units capable of accelerating graph processing applications. GraphS can be leveraged to greatly reduce energy consumption dealing with underlying adjacency matrix computations, eliminating unnecessary off-chip accesses and providing ultra-high internal bandwidth. The device-to-architecture co-simulation for three social network data-sets indicate roughly 3.6× higher energy-efficiency and 5.3× speed-up over recent ReRAM crossbar. It achieves ~4× higher energy-efficiency and 5.1× speed-up over recent processing-in-DRAM acceleration methods.