FANNS: An FPGA-Based Approximate Nearest-Neighbor Search Accelerator
Wei Yuan, Xi Jin · IEEE Transactions on Very Large Scale Integration (VLSI) Systems · 2025
Approximate nearest-neighbor search (ANNS) based on high-dimensional vectors has been extensively utilized in data science and neural networks. However, deploying ANNS in production systems requires minimal redundant computation, high recall rates, and low on-chip memory usage, which existing hardware accelerators fail to offer. We propose FANNS, a solution for ANNS based on high-dimensional vectors that can eliminate redundant computations and reuse on-chip data. Extensive evaluations show that FANNS achieves an average of$184.1\times $,$33.0\times $,$2.9\times $, and$2.5\times $better energy efficiency than CPUs, GPUs, and two state-of-the-art ANNS architectures, i.e., DF-GAS and Vstore, respectively.