GHVSA: Graph-based high-dimensional vector search accelerator

Graph-based high-dimensional vector search is the most common method for retrieval and achieves the best performance. However, implementing graph-based high-dimensional vector search in industrial applications requires reduced redundant computation, high accuracy, and low latency, which existing works fail to provide. We propose GHVSA, a graph-based vector search architecture that achieves low on-chip memory footprint and high energy efficiency by narrowing down the scope of vector search (about 0.5% of the entire dataset), avoiding redundant computation(more than 50% in the distance computation phase and more than 90% in the sorting phase) and data reuse. Extensive evaluations on three datasets show that GHVSA achieves an average of 609.07×, 53.6×, 5.16×, and 6.06× better energy efficiency than CPUs, GPUs, DF-GAS, and VStore, respectively.