AMSNet: Analog Memristive System Architecture for Mean-Pooling with Dropout Convolutional Neural Network

Abstract

This work proposes analog hardware implementation of Mean-Pooling Convolutional Neural Network (CNN) with 50% random dropout backpropagation training. We illustrate the effect of variabilities of real memristive devices on the performance of CNN, and tolerance to the input noise. The classification accuracy of CNN is approximately 93% independent on memristor variabilities and input noise. On-chip area and power consumption of analog 180nm CMOS CNN with WOx memristors are 0.09338995mm2 and 3.3992W, respectively.