Accelerating Position-Aware Top-k ListNet for Ranking Under Custom Precision Regimes

Document ranking is used to order query results by relevance with ranking models. ListNet is a well-know ranking approach for constructing and training learning to rank models. Compared with traditional learning approaches, ListNet delivers better accuracy, but is computationally too expensive to learn models with large datasets due to the large number of permutations involved in computing the gradients. This paper introduces a position-aware sampling approach, which takes the importance of ranking positions into account and shows better accuracy than previous sampling methods. We also propose an effective quantisation method based on FPGA devices for the ListNet algorithm, which organises the gradient values to several batches, and associates each batch with a different fractional precision. We implemented our approach on a Xilinx Ultrascale+ board and applied it to the MQ 2008 benchmark dataset for ranking. The experiment results show a 4.42x speedup over an Nvidia GTX 1080T GPU implementation with 2% accuracy loss.