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

Olga Krestinskaya, Adilya Bakambekova, Alex Pappachen James

2019

DOI 10.1109/aicas.2019.8771611

Data confidence 70 medium

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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 WOxmemristors are 0.09338995mm2and 3.3992W, respectively.