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Title: Piecewise empirical model (PEM) of resistive memory for pulsed analog and neuromorphic applications

Abstract

With the end of Dennard scaling and the ever-increasing need for more efficient, faster computation, resistive switching devices (ReRAM), often referred to as memristors, are a promising candidate for next generation computer hardware. These devices show particular promise for use in an analog neuromorphic computing accelerator as they can be tuned to multiple states and be updated like the weights in neuromorphic algorithms. Modeling a ReRAM-based neuromorphic computing accelerator requires a compact model capable of correctly simulating the small weight update behavior associated with neuromorphic training. These small updates have a nonlinear dependence on the initial state, which has a significant impact on neural network training. Consequently, we propose an empirically derived general purpose model that can accurately capture the nonlinearity of an arbitrary two-terminal device to match pulse measurements important for neuromorphic computing applications. By defining the state of the device to be proportional to its current, the model parameters can be extracted from a series of voltages pulses that mimic the behavior of a device in an analog neuromorphic computing accelerator. This allows for a general, accurate, and intuitive compact circuit model that is applicable to different resistance switching device technologies. In this work we explain the detailsmore » of the model, implement the model in the circuit simulator Xyce, and give an example of its usage to model a specific Ta/TaOx device.« less

Authors:
 [1];  [2];  [1];  [1];  [1];  [1];  [1];  [1]
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  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  2. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Sandia National Laboratories (SNL-CA), Livermore, CA (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1469639
Report Number(s):
SAND2018-9642J
Journal ID: ISSN 1569-8025; PII: 1107
Grant/Contract Number:  
AC04-94AL85000
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Computational Electronics
Additional Journal Information:
Journal Volume: 16; Journal Issue: 4; Journal ID: ISSN 1569-8025
Publisher:
Springer
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Niroula, John, Agarwal, Sapan, Jacobs-Gedrim, Robin, Schiek, Richard L., Hughart, David, Hsia, Alex, James, Conrad D., and Marinella, Matthew J. Piecewise empirical model (PEM) of resistive memory for pulsed analog and neuromorphic applications. United States: N. p., 2017. Web. doi:10.1007/s10825-017-1107-3.
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Niroula, John, Agarwal, Sapan, Jacobs-Gedrim, Robin, Schiek, Richard L., Hughart, David, Hsia, Alex, James, Conrad D., & Marinella, Matthew J. Piecewise empirical model (PEM) of resistive memory for pulsed analog and neuromorphic applications. United States. https://doi.org/10.1007/s10825-017-1107-3
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Niroula, John, Agarwal, Sapan, Jacobs-Gedrim, Robin, Schiek, Richard L., Hughart, David, Hsia, Alex, James, Conrad D., and Marinella, Matthew J. Sat . "Piecewise empirical model (PEM) of resistive memory for pulsed analog and neuromorphic applications". United States. https://doi.org/10.1007/s10825-017-1107-3. https://www.osti.gov/servlets/purl/1469639.
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@article{osti_1469639,
title = {Piecewise empirical model (PEM) of resistive memory for pulsed analog and neuromorphic applications},
author = {Niroula, John and Agarwal, Sapan and Jacobs-Gedrim, Robin and Schiek, Richard L. and Hughart, David and Hsia, Alex and James, Conrad D. and Marinella, Matthew J.},
abstractNote = {With the end of Dennard scaling and the ever-increasing need for more efficient, faster computation, resistive switching devices (ReRAM), often referred to as memristors, are a promising candidate for next generation computer hardware. These devices show particular promise for use in an analog neuromorphic computing accelerator as they can be tuned to multiple states and be updated like the weights in neuromorphic algorithms. Modeling a ReRAM-based neuromorphic computing accelerator requires a compact model capable of correctly simulating the small weight update behavior associated with neuromorphic training. These small updates have a nonlinear dependence on the initial state, which has a significant impact on neural network training. Consequently, we propose an empirically derived general purpose model that can accurately capture the nonlinearity of an arbitrary two-terminal device to match pulse measurements important for neuromorphic computing applications. By defining the state of the device to be proportional to its current, the model parameters can be extracted from a series of voltages pulses that mimic the behavior of a device in an analog neuromorphic computing accelerator. This allows for a general, accurate, and intuitive compact circuit model that is applicable to different resistance switching device technologies. In this work we explain the details of the model, implement the model in the circuit simulator Xyce, and give an example of its usage to model a specific Ta/TaOx device.},
doi = {10.1007/s10825-017-1107-3},
journal = {Journal of Computational Electronics},
number = 4,
volume = 16,
place = {United States},
year = {Sat Dec 02 00:00:00 EST 2017},
month = {Sat Dec 02 00:00:00 EST 2017}
}
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https://doi.org/10.1007/s10825-017-1107-3
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