In this paper, we firstly introduce a method to efficiently implement large-scale high-dimensional convolution with realistic memristor-based circuit components. An experiment verified simulator is adapted for accurate prediction of analog crossbar behavior. An improved conversion algorithm is developed to convert convolution kernels to memristor-based circuits, which minimizes the error with consideration of the data and kernel patterns in CNNs. With circuit simulation for all convolution layers in ResNet-20, we found that 8-bit ADC/DAC is necessary to preserve software level classification accuracy.
@article{arxiv.1810.02225,
title = {Memristor-based Deep Convolution Neural Network: A Case Study},
author = {Fan Zhang and Miao Hu},
journal= {arXiv preprint arXiv:1810.02225},
year = {2018}
}