In-Memory PageRank Accelerator with a Cross-Point Array of Resistive Memories

Abstract

In-memory computing with cross-point arrays of resistive memory is a promising technique for typical tasks, such as the training and inference of deep learning. Recently, it has been shown that a cross-point array of resistive switching memory (RRAM) with a feedback configuration can be used to solve linear systems, compute eigenvectors, and rank webpages in just one step. Here, we demonstrate the PageRank with a real data set (the Harvard500) and an eight-level RRAM model, describing the conductance update, the standard deviation of each level, and the conductance ratio. By carefully placing each memory conductance value via a program-verify technique, we show that an accuracy of 95% can be achieved for the ranking result. The equivalent throughput of the eigenvector circuit for PageRank is estimated to be 0.183 tera-operations per second (TOPS), while the energy efficiency is 362 TOPS/W. This article supports the feasibility of in-memory PageRank with significant improvements in speed and energy efficiency for practical big-data tasks.