In Memory PageRank Accelerator With a Cross Point Array of Resistive Memories
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.
IEEE Transactions on Electron Devices