An SSD-Based Accelerator for Singular Value Decomposition Recommendation Algorithm on Edge

Recommender system (RS) is widely used in social networks, computational advertising, video platforms and many other Internet applications. Most RSs are based on the cloud-to-edge framework. Recommended item lists are computed in the cloud server and then transmitted to the edge device. Network bandwidth and latency between cloud server and edge may cause the delays in recommendation. Edge computing could help obtain user's real-time preferences and thus improve the performance of recommendation. However, the increasing complexity of rec-ommendation algorithms and data scales cause challenges to real-time recommendation on edge. To solve these problems, in this paper, we mainly focus on the Jacobi-based singular value decomposition (SVD) algorithm because of its high parallel processing potential and cost effective NVM-storage. We propose an SSD-based accelerator for the one-sided Jacobi transformation algorithm. We implement a hardware prototype on a real Xilinx FPGA development board. Experimental results show that the proposed SVD engine can achieve 3.4x speedup to 5.8x speedup compared with software SVD solvers such as MATLAB running on a high-performance CPU.