Gzippo: Highly-compact Processing-In-Memory Graph Accelerator Alleviating Sparsity and Redundancy

Graph application plays a significant role in real-world data computation. However, the memory access patterns become the performance bottleneck of the graph applications, which include low compute-to-communication ratio, poor temporal locality, and poor spatial locality. Existing RRAM-based processing-in-memory accelerators reduce the data movements but fail to address both sparsity and redundancy of graph data. In this work, we present Gzippo, a highly-compact design that supports graph computation in the compressed sparse format. Gzippo employs a tandem-isomorphic-crossbar architecture both to eliminate redundant searches and sequential indexing during iterations, and to remove sparsity leading to non-effective computation on zero values. Gzippo achieves a 3.0x (up to 17.4x) performance speedup, 23.9x (up to 163.2x) energy efficiency over state-of-the-art RRAM-based PIM accelerator, respectively.