@inproceedings{b1581c137fbc45179195330f5b7da6a4,
title = "Learning to predict IR drop with effective training for ReRAM-based neural network hardware",
abstract = "Due to the inevitability of the IR drop problem in passive ReRAM crossbar arrays, finding a software solution that can predict the effect of IR drop without the need of expensive SPICE simulations, is very desirable. In this paper, two simple neural networks are proposed as software solution to predict the effect of IR drop. These networks can be easily integrated in any deep neural network framework to incorporate the IR drop problem during training. As an example, the proposed solution is integrated in BinaryNet framework and the test validation results, done through SPICE simulations, show very high improvement in performance close to the baseline performance, which demonstrates the efficacy of the proposed method. In addition, the proposed solution outperforms the prior work on challenging datasets such as CIFAR10 and SVHN.",
keywords = "0T1R, Binary neural network, Deep neural network, IR drop, ReRAM crossbar array",
author = "Sugil Lee and Giju Jung and Fouda, {Mohammed E.} and Jongeun Lee and Ahmed Eltawil and Fadi Kurdahi",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 57th ACM/IEEE Design Automation Conference, DAC 2020 ; Conference date: 20-07-2020 Through 24-07-2020",
year = "2020",
month = jul,
doi = "10.1109/DAC18072.2020.9218735",
language = "English (US)",
series = "Proceedings - Design Automation Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2020 57th ACM/IEEE Design Automation Conference, DAC 2020",
address = "United States",
}