@inproceedings{71570ac2f2d5462e84281706896fe31b,
title = "Memristive non-idealities: Is there any practical implications for designing neural network chips?",
abstract = "The impact of device-to-device, cycle-to-cycle, and parasitic variations in memristor devices on the performance of neural network architectures is not a fully understood topic. In this paper, we present an explicit analysis of memristor variabilities and non-idealities of memristive crossbar based learning architectures. The measurements of real devices and their effects on dot product operation in a memristive crossbar is reported. The effect of these non-idealities, limited resistive levels and variabilities on the performance and reliability of two-layer Artificial Neural Network (ANN), Convolutional Neural Network (CNN) and Binary Neural Network (BNN) is analyzed and presented.",
keywords = "Learning Architectures, Memristive Crossbar, Memristor, Variability",
author = "Olga Krestinskaya and Aidana Irmanova and Alex James",
year = "2019",
month = jan,
day = "1",
doi = "10.1109/ISCAS.2019.8702245",
language = "English",
series = "Proceedings - IEEE International Symposium on Circuits and Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Proceedings",
address = "United States",
note = "2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 ; Conference date: 26-05-2019 Through 29-05-2019",
}