RelHD: A Graph-based Learning on FeFET with Hyperdimensional Computing

Advances in graph neural network (GNN)-based algorithms enable machine learning on relational data. GNNs are computationally demanding since they rely upon backpropagation over the graph data that has sparse and irregular characteristics. In this paper, we propose a lightweight graph-based machine learning framework based on hyperdimensional computing (HDC) called RelHD. It maps the features of each node into a high-dimensional space and embeds relationships between nodes. Using lightweight HDC operations, RelHD enables both training and inference on graph data without backpropagation. Furthermore, we design a scalable processing in-memory (PIM) architecture based on the emerging FeFET technology to accelerate the proposed algorithm. Our strategy optimizes data allocation and operation scheduling that maximizes the accelerator performance by addressing the sparseness and irregularity of the graph. Experimental results show that RelHD offers comparable accuracy to the popular GNN-based algorithms while being up to 32× faster on GPU. Also, our FeFET-based accelerator achieves 33× of speedup and 59287× energy efficiency improvement on average over the GPU. It is 10× faster and 986× more energy efficient on average compared to the state-of-the-art in-memory processing-based GNN accelerator.