IMGA: Efficient In-Memory Graph Convolution Network Aggregation With Data Flow Optimizations

Abstract

Aggregating features from neighbor vertices is a fundamental operation in Graph Convolution Network (GCN). However, the sparsity in graph data creates poor spatial and temporal locality, causing dynamic and irregular memory access patterns and limiting the performance of aggregation on the Von Neumann architecture. The emerging processing-in-memory (PIM) architecture is based on emerging non-volatile memory (NVM), like Spin-orbit torque Magnetic RAM (SOT-MRAM), and demonstrates promising prospects in alleviating the Von Neumann bottleneck. However, the limited memory capacity of PIM medium still incurs non-negligible data movements between PIM architecture and external memory. To solve this challenge, we propose a SOT-MRAM based in-memory computing architecture, called IMGA, for efficient in-situ graph aggregation. Specifically, we design adaptive data flow management strategies that reuse vertex data in MRAM when processing graphs of different scales and adopt edge data as the control signal source to utilize the graph’s structural information. A reordering optimization strategy leveraging hardware-software co-design principle is proposed to further reduce the costly data movement. Experimental results demonstrate that IMGA achieves an average 2523x and 21x speedup, and 1.03E+6 and 1.04E+3 energy efficiency compared with CPU and GPU, respectively.