A heterogeneous 3-D stacked PIM accelerator for GCN-based recommender systems

Authors:
raw_author_name: Xinyang Shen
raw_affiliation_string: National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Clusters and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Luoyu Road, Wuhan, 430074, Hubei, China
raw_author_name: Yu Huang
raw_affiliation_string: National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Clusters and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Luoyu Road, Wuhan, 430074, Hubei, China
raw_author_name: Long Zheng
raw_affiliation_string: National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Clusters and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Luoyu Road, Wuhan, 430074, Hubei, China
raw_author_name: Xiaofei Liao
raw_affiliation_string: National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Clusters and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Luoyu Road, Wuhan, 430074, Hubei, China
raw_author_name: Hai Jin
raw_affiliation_string: National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Clusters and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Luoyu Road, Wuhan, 430074, Hubei, China

Abstract: Abstract Modern recommendation systems integrate graph convolution neural networks (GCN) for enhancing embedding representation. Compared with widely deployed neural network-based models, the extra message propagation layer of GCN-based recommendation is featured with extensive computations and irregular memory access. However, architecture designs for prevailing deep neural network recommendation models assume simple pooling in the embedding layer. ReRAM-based GCN accelerators are specialized for graph-related operations. However, they are designed for general graphs, while GCN-based recommendation models mainly operate on the user-item graph. In this paper, we proposed a resistive random accessed memory (ReRAM) based processing-in-memory (PIM) accelerator, ReGCNR, for GCN-based recommendation. ReGCNR is featured with three key innovations. First, we exploit the 3-dimensional (3-D) stacked heterogeneous ReRAM to fit with the large-size embedding table and user-item graph. Then, we propose a joint degree mapping schema that maximizes the efficiency of the execution pipeline. After that, ReGCNR assembles a well-coordinated pipeline and hardware scheduling design to boost overall system performance. Results show that ReGCNR outperforms GPU by 69.83 $$\times$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>×</mml:mo> </mml:math> and 56.67 $$\times$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>×</mml:mo> </mml:math> in terms of average speedup and energy saving, respectively. In addition, ReGCNR outperforms state-of-the-art ReRAM-based solutions by 11.13 $$\times$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>×</mml:mo> </mml:math> speedups and 7.22 $$\times$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>×</mml:mo> </mml:math> energy savings on average.