Empirical metal-oxide RRAM device endurance and retention model for deep learning simulations

Lammie, Corey, Rahimiazghadi, Mostafa, and Ielmini, Daniele (2021) Empirical metal-oxide RRAM device endurance and retention model for deep learning simulations. Semiconductor Science and Technology, 36 (6). 065003.

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View at Publisher Website: https://doi.org/10.1088/1361-6641/abf29d
 
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Abstract

Memristive devices including resistive random access memory (RRAM) cells are promising nanoscale low-power components projected to facilitate significant improvement in power and speed of Deep Learning (DL) accelerators, if structured in crossbar architectures. However, these devices possess non-ideal endurance and retention properties, which should be modeled efficiently. In this paper, we propose a novel generalized empirical metal-oxide RRAM endurance and retention model for use in large-scale DL simulations. To the best of our knowledge, the proposed model is the first to unify retention-endurance modeling while taking into account time, energy, SET-RESET cycles, device size, and temperature. We compare the model to state-of-the-art and demonstrate its versatility by applying it to experimental data from fabricated devices. Furthermore, we use the model for CIFAR-10 dataset classification using a large-scale deep memristive neural network (DMNN) implementing the MobileNetV2 architecture. Our results show that, even when ignoring other device non-idealities, retention and endurance losses significantly affect the performance of DL networks. Our proposed model and its DL simulations are made publicly available.

Item ID: 68688
Item Type: Article (Research - C1)
ISSN: 1361-6641
Copyright Information: © 2021 IOP Publishing Ltd. Accepted Author version: CC BY-NC-ND 4.0
Funders: James Cook University (JCU)
Date Deposited: 20 Jul 2021 21:58
FoR Codes: 46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461103 Deep learning @ 50%
40 ENGINEERING > 4018 Nanotechnology > 401804 Nanoelectronics @ 50%
SEO Codes: 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280110 Expanding knowledge in engineering @ 100%
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