research-article

A CMOS-memristive self-learning neural network for pattern classification applications

Authors:

Melika Payvand,

Avantika Sodhi,

Luke TheogarajanAuthors Info & Claims

NANOARCH '14: Proceedings of the 2014 IEEE/ACM International Symposium on Nanoscale Architectures

Pages 92 - 97

https://doi.org/10.1145/2770287.2770311

Published: 08 July 2014 Publication History

Abstract

Memristors have proven to be powerful analogs of neural synapses. While there have been some efforts to exploit this feature, the intrinsic analog nature of the memristive element has not been fully utilized. This paper presents a hardware-efficient neuromorphic CMOS-memristor pattern classifier. The system takes advantage of the memristor as a true analog memory, and Spike Timing Dependent Plasticity (STDP) is utilized to program memristors in a recurrent neural network. System co-simulations are performed in Verilog-AMS with CMOS devices and previously published memristive models. The results indicate the power of this approach in pattern classification using unsupervised learning.

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Cited By

Huang JSerb AStathopoulos SProdromakis T(2023)Text classification in memristor-based spiking neural networksNeuromorphic Computing and Engineering10.1088/2634-4386/acb2f03:1(014003)Online publication date: 31-Jan-2023
https://doi.org/10.1088/2634-4386/acb2f0
Lieske TBiglari MFey DJacob B(2018)Multi-level memristive voltage dividerProceedings of the International Symposium on Memory Systems10.1145/3240302.3240430(259-268)Online publication date: 1-Oct-2018
https://dl.acm.org/doi/10.1145/3240302.3240430

Index Terms

A CMOS-memristive self-learning neural network for pattern classification applications

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cover image ACM Conferences

NANOARCH '14: Proceedings of the 2014 IEEE/ACM International Symposium on Nanoscale Architectures

July 2014

193 pages

ISBN:9781450328340

DOI:10.1145/2770287

General Chairs:
Jacques-Olivier Klein
University Paris-Sud
,
Csaba Andras Moritz
UMass Amherst and BlueRISC
,
Program Chair:
Sorin Cotofana
Delft University of Technology

Copyright © 2014 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 July 2014

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Air Force Office of Scientific Research (AFOSR)

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Nanoarch '14

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SIGDA

Nanoarch '14: IEEE/ACM International Symposium on Nanoscale Architectures

July 8 - 10, 2014

France, Paris

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Cited By

Huang JSerb AStathopoulos SProdromakis T(2023)Text classification in memristor-based spiking neural networksNeuromorphic Computing and Engineering10.1088/2634-4386/acb2f03:1(014003)Online publication date: 31-Jan-2023
https://doi.org/10.1088/2634-4386/acb2f0
Lieske TBiglari MFey DJacob B(2018)Multi-level memristive voltage dividerProceedings of the International Symposium on Memory Systems10.1145/3240302.3240430(259-268)Online publication date: 1-Oct-2018
https://dl.acm.org/doi/10.1145/3240302.3240430

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