In-memory PageRank using a Crosspoint Array of Resistive Switching Memory (RRAM) devices

Thanks to the high parallelism endowed by physical rules, in-memory computing with crosspoint resistive memory arrays has been applied to accelerate typical dataintensive tasks such as the training and inference of deep learning. Recently, it has been shown that a crosspoint resistive switching memory (RRAM) circuit 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 database (the Harvard500) together with an 8-level RRAM model that is based on experimental measurements, including the max/min conductance ratio, the high conductance range and the standard deviation of each level. By using a verify algorithm for the RRAM device programming, the PageRank result from the crosspoint circuit shows a cosine similarity of 93.5% with respect to the floating-point solution. With more discrete conductance levels and a broader high conductance range in the RRAM model, a better performance of cosine similarity up to 97% can be achieved. This work supports the feasibility of in-memory PageRank with realistic RRAM devices for real-world networks.