HygHD: Hyperdimensional Hypergraph Learning

Hypergraphs can model real-world data that has higher-order relationships. Graph neural network (GNN)-based solutions emerged as a hypergraph learning solution, but they face non-uniform memory accesses and accompany memory-intensive and compute-intensive operations, making the acceleration with near-data processing challenging. We propose a hyperdimensional computing (HDC)-based hypergraph learning framework called HygHD, which consists of highly parallelizable and lightweight HDC operations. HygHD accelerates both the training and inference on ferroelectric field-effect transistor (FeFET)-based processing-in-memory (PIM) hardware. Furthermore, we devise a hardware-friendly block-level concatenation and fine-grained block-level scheduler for high efficiency. Our evaluation results show that HygHD offers comparable accuracy to existing GNN-based solutions. Also, HygHD on GPU is up to 443× (7.67×) faster and 142× (2.78×) more energy efficient in training (inference) than the fastest GNN-based approach [1] on GPU. The HygHD accelerator further accelerates the HygHD algorithm, providing an average speedup of 40.0× (3.41×) on training (inference) compared to the HygHD GPU implementation.