FPGA-Accelerated Data Preprocessing for Personalized Recommendation Systems

Abstract

Deep neural network (DNN)-based recommendation systems (RecSys) are one of the most successfully deployed machine learning applications in commercial services for predicting ad click-through rates or rankings. While numerous prior work explored hardware and software solutions to reduce the training time of RecSys, its end-to-end training pipeline including the data preprocessing stage has received little attention. In this work, we provide a comprehensive analysis of RecSys data preprocessing, root-causing the feature generation and normalization steps to cause a major performance bottleneck. Based on our characterization, we explore the efficacy of an FPGA-accelerated RecSys preprocessing system that achieves a significant 3.4-12.1x end-to-end speedup compared to the baseline CPU-based RecSys preprocessing system.