FCCQA: A High-Performance FPGA-based Accelerator for Approximate Nearest Neighbor Search

Data search plays a key role in information retrieval and machine learning. Currently, the Field Programmable Gate Array (FPGA) has emerged as a popular hardware accelerator for such studies. In order to maintain a better balance between search time and accuracy, we introduce FCCQA, an exhaustive and scalable data search architecture, utilizing Approximate Nearest Neighbor (ANN) for rapid interrogation of high-dimensional datasets on FPGA. Our architecture adopts the technique of compositing quantization to achieve a high data compression ratio, reducing memory usage to about 1.67%, while efficiently transforming the intricate computation into a simplified table lookup operation in high-dimensional datasets. Compared with GPU and CPU, our approach establishes a superior balance among accuracy, energy efficiency and speed.