WooKong: A Ubiquitous Accelerator for Recommendation Algorithms with Custom Instruction Sets on FPGA
Chao Wang, Lei Gong, Xiang Ma, Xi Li, Xuehai Zhou
Abstract
Recommendation algorithms, such as Neighborhood-based Collaborative- Filtering (CF), have been widely applied in various emerging machine learning applications. However, under the circumstance of the explosive big data, it poses significant challenges to CF recommendation algorithms as it is becoming quite time and energy-consuming. It has to be optimized and accelerated by powerful engines to process on large data scale. To solve these problems, in this article, we propose WooKong, a ubiquitous accelerator architecture for the collaborative-filtering recommendation on FPGA. It is able to accommodate three types of CF recommendation algorithms, including User-based CF, Item-based CF, and SlopeOne recommendations algorithms, with five different similarity analysis metrics including Jaccard, Cosine, CosineIR, euclidean, and Pearson. To maintain flexibility for these different CF algorithms and metrics, we adopt custom instruction sets to manipulate the learning and prediction accelerators. We implement a hardware prototype on a real Xilinx Zynq FPGA development board. Experimental results show that the proposed learning and prediction accelerators can achieve 8.0X speedup and 1.7X speedup compared with an Intel i7 processor respectively. The accelerator has the energy benefits of up to 137.4X compared with an NVIDIA Tesla K40C GPU, with the affordable hardware cost.