Signal Integrity Modeling and Analysis of Large-Scale Memristor Crossbar Array in a High-Speed Neuromorphic System for Deep Neural Network

Abstract

In this article, we modeled, analyzed, and evaluated a large-scale memristor crossbar array in a neuromorphic system for a deep neural network (DNN) considering signal integrity (SI). Since hardware-based DNN using a memristor crossbar array operates in an analog way, it has serious reliability problems caused by interconnects, driver, and nonlinear memory cells. The interconnects including an on-chip signal and power/ground (P/G) mesh were modeled as circuit parameters from a full 3-D-electromagnetic (EM) simulation. The memristor was electrically modeled including its nonlinear characteristics. These models were configured into a 512 ×512 memristor crossbar array with drivers and peripheral circuits for the implementation of DNN. Then, we analyzed the component-level SI and nonlinearity for the interconnects and memristors, and the system level for the total array configuration. Finally, the regression of DNN in the memristor crossbar array was evaluated for verification of the analysis. All the analyzed SI and nonlinearity effects from the interconnects, memristor, and array configuration affected the regression. As the input voltage level decreased, the effect of the SI effect on accuracy became more dominant than that of the nonlinearity of the memristor effect. In terms of SI, it was verified that there is a tradeoff relationship between IR drop and crosstalk according to the interconnects' size. Finally, the accuracy and power consumption were verified according to the array configuration in the system level as an important issue of the memristor crossbar array. Through the overall process, it was possible to analyze how the SI and nonlinearity effects affect the computational results in the neuromorphic system.