TCAM-GNN: A TCAM-Based Data Processing Strategy for GNN Over Sparse Graphs

The graph neural network (GNN) has recently become an emerging research topic for processing non- euclidean data structures since the data used in various popular application domains are usually modeled as a graph, such as social networks, recommendation systems, and computer vision. Previous GNN accelerators com- monly utilize the hybrid architecture to resolve the issue of “hybrid computing pattern” in GNN training. Neverthe- less, the hybrid architecture suffers from poor utilization of hardware resources mainly due to the dynamic work- loads between different phases in GNN. To address these issues, existing GNN accelerators adopt a unified structure with numerous processing elements and high bandwidth emory. However, the large amount of data movement between the processor and memory could heavily down- grade the performance of such accelerators in real-world graphs. As a result, the processing-in-memory architecture, such as the ReRAM-based crossbar, becomes a promising solution to reduce the memory overhead of GNN training. In this work, we present the TCAM-GNN, a novel TCAM-based data processing strategy, to enable high-throughput and energy-efficient GNN training over ReRAM-based crossbar architecture. Several hardware co-designed data structures and placement methods are proposed to fully exploit the parallelism in GNN during training. In addition, we propose a dynamic fixed-point formatting approach to resolve the precision issue. An adaptive data reusing policy is also pro- posed to enhance the data locality of graph features by the bootstrapping batch sampling approach. Overall, TCAM- GNN could enhance computing performance by 4.25× and energy efficiency by 9.11× on average compared to the neural network accelerators.