Efficient Large-scale Approximate Nearest Neighbor Search on the OpenCL-FPGA


We present a new method for Product Quantization (PQ) based approximated nearest neighbor search (ANN) in high dimensional spaces. Specifically, we first propose a quantization scheme for the codebook of coarse quantizer, product quantizer, and rotation matrix, to reduce the cost of accessing these codebooks. Our approach also combines a highly parallel k-selection method, which can be fused with the distance calculation to reduce the memory overhead. We implement the proposed method on Intel HARPv2 platform using OpenCL-FPGA. The proposed method significantly outperforms state-of-the-art methods on CPU and GPU for high dimensional nearest neighbor queries on billion-scale datasets in terms of query time and accuracy regardless of the batch size. To our best knowledge, this is the first work to demonstrate FPGA performance superior to CPU and GPU on high-dimensional, large-scale ANN datasets.

Conference on Computer Vision and Pattern Recognition (CVPR), Jun, 2018

(Acceptance Rate: underline29%, 979 out of over 3300)

Jialiang Zhang
PhD Candidate

Expert in hardware acceleration of big data and machine learning applications using FPGA and emerging memory technologies