Modeling and Demonstration of Hardware-based Deep Neural Network (DNN) Inference using Memristor Crossbar Array considering Signal Integrity

Abstract

A hardware-based artificial intelligence (AI) operation using memristor crossbar array is a promising AI computing architecture due to its energy-efficiency. It mimics the computational form of matrix-vector multiplication, which is the main AI operation and is implemented in an analog way. However, the reliability problem is serious because of the hardware-based operation. In this paper, we propose a hybrid circuit model of a hardware-based deep neural network (DNN) for a large-scale memristor crossbar array in terms of signal integrity (SI) problems. After DNN classification training for the optimized weight matrix in memristors, we demonstrated and analyzed the effect of SI on DNN accuracy using the proposed model. It is possible to analyze the effect of the SI problems due to interconnection at the crossbar on the reliability of computational accuracy through this hybrid circuit model. Simulated accuracy of DNN inference is degraded up to 36.4% in the worst case due to IR drop and ringing depending on the physical dimension of array interconnection and operating frequency in a memristor crossbar array.