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Title: Memristor design rules for dynamic learning and edge processing applications

Abstract

The ability to dynamically learn and adapt to changes in the environment is one of the hallmarks of biological systems. In this work, we identify the subset of the design space of memristive materials that is optimal for dynamic learning applications. Using an architecture inspired on the learning center of the insect brain, we implement a model system consisting of a discrete implementation of spiking neurons where dynamic learning takes place on a set of plastic synapses formed by memristor pairs in a crossbar array. Using two separate benchmarks, one comprising the dynamic learning of the Modified National Institute of Standards and Technology dataset and another one targeting one shot learning, we have identified the key properties that memristive materials should have to be optimal dynamic learners. The results obtained show that a fine degree of control of the memristor internal state is key to achieve high classification accuracy during dynamic learning but that within this optimal region learning is extremely robust both to device variability and to errors in the writing of the internal state, in all cases allowing for 2 sigma variations greater than 40% without significant loss of accuracy, hence overcoming two of the perceived limitations ofmore » memristors. By additionally requiring that learning takes place concurrently to information processing, we are able to derive a set constraints to the memristor dynamics.« less

Authors:
ORCiD logo [1]
  1. Argonne National Lab. (ANL), Argonne, IL (United States)
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
U.S. Department of Defense (DOD), Defense Advanced Research Projects Agency (DARPA); USDOE Office of Science (SC)
OSTI Identifier:
1578165
Alternate Identifier(s):
OSTI ID: 1560347
Grant/Contract Number:  
AC02-06CH11357
Resource Type:
Accepted Manuscript
Journal Name:
APL Materials
Additional Journal Information:
Journal Volume: 7; Journal Issue: 9; Journal ID: ISSN 2166-532X
Publisher:
American Institute of Physics (AIP)
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; Artificial neural networks; Machine learning; Ferroelectric materials; Resistive switching; Electrical properties and parameters; Nanowires; Memristor

Citation Formats

Yanguas-Gil, Angel. Memristor design rules for dynamic learning and edge processing applications. United States: N. p., 2019. Web. https://doi.org/10.1063/1.5109910.
Yanguas-Gil, Angel. Memristor design rules for dynamic learning and edge processing applications. United States. https://doi.org/10.1063/1.5109910
Yanguas-Gil, Angel. Thu . "Memristor design rules for dynamic learning and edge processing applications". United States. https://doi.org/10.1063/1.5109910. https://www.osti.gov/servlets/purl/1578165.
@article{osti_1578165,
title = {Memristor design rules for dynamic learning and edge processing applications},
author = {Yanguas-Gil, Angel},
abstractNote = {The ability to dynamically learn and adapt to changes in the environment is one of the hallmarks of biological systems. In this work, we identify the subset of the design space of memristive materials that is optimal for dynamic learning applications. Using an architecture inspired on the learning center of the insect brain, we implement a model system consisting of a discrete implementation of spiking neurons where dynamic learning takes place on a set of plastic synapses formed by memristor pairs in a crossbar array. Using two separate benchmarks, one comprising the dynamic learning of the Modified National Institute of Standards and Technology dataset and another one targeting one shot learning, we have identified the key properties that memristive materials should have to be optimal dynamic learners. The results obtained show that a fine degree of control of the memristor internal state is key to achieve high classification accuracy during dynamic learning but that within this optimal region learning is extremely robust both to device variability and to errors in the writing of the internal state, in all cases allowing for 2 sigma variations greater than 40% without significant loss of accuracy, hence overcoming two of the perceived limitations of memristors. By additionally requiring that learning takes place concurrently to information processing, we are able to derive a set constraints to the memristor dynamics.},
doi = {10.1063/1.5109910},
journal = {APL Materials},
number = 9,
volume = 7,
place = {United States},
year = {2019},
month = {9}
}

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    Works referencing / citing this record:

    Emerging materials in neuromorphic computing: Guest editorial
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