An Energy-Efficient Graphene-based Spiking Neural Network Architecture for Pattern Recognition

Nicoleta Cucu Laurencin; Charles Timmermans; Sorin D. Cotofana

doi:10.1109/ISCAS58744.2024.10558243

An Energy-Efficient Graphene-based Spiking Neural Network Architecture for Pattern Recognition

Nicoleta Cucu Laurencin^*, Charles Timmermans^*, Sorin D. Cotofana

^*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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Abstract

In this paper we propose a generic graphene-based Spiking Neural Network (SNN) architecture for pattern recognition and the associated weight values initialization methodology. The SNN has a Winner-Takes-All 3-layer structure and exhibits tuneable recognition accuracy by exploiting interpatterns similarity/dissimilarity. To demonstrate the capabilities of our proposal we present an SNN instance tailored for low resolution MNIST handwritten digits recognition and evaluate its recognition accuracy by means of SPICE simulations. 2 voltage levels are initially utilized for synaptic weight values representation and the recognition accuracy varies from 75.8% to 99.2%, which, together with its compactness and energy efficient (pJ range/spike), suggests that our approach has great potential for edge device implementations.

Original language	English
Title of host publication	ISCAS 2024 - IEEE International Symposium on Circuits and Systems
Publisher	IEEE
Number of pages	5
ISBN (Electronic)	9798350330991
DOIs	https://doi.org/10.1109/ISCAS58744.2024.10558243
Publication status	Published - 2024
Event	2024 IEEE International Symposium on Circuits and Systems, ISCAS 2024 - Singapore, Singapore Duration: 19 May 2024 → 22 May 2024

Publication series

Name	Proceedings - IEEE International Symposium on Circuits and Systems
ISSN (Print)	0271-4310

Conference

Conference	2024 IEEE International Symposium on Circuits and Systems, ISCAS 2024
Country/Territory	Singapore
City	Singapore
Period	19/05/24 → 22/05/24

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

GNR
Graphene
Nanoelectronics
Neuromorphic Computing
Pattern Recognition
Spiking Neural Network

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10.1109/ISCAS58744.2024.10558243

An_Energy-Efficient_Graphene-based_Spiking_Neural_Network_Architecture_for_Pattern_RecognitionFinal published version, 1.48 MB

Cite this

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title = "An Energy-Efficient Graphene-based Spiking Neural Network Architecture for Pattern Recognition",

abstract = "In this paper we propose a generic graphene-based Spiking Neural Network (SNN) architecture for pattern recognition and the associated weight values initialization methodology. The SNN has a Winner-Takes-All 3-layer structure and exhibits tuneable recognition accuracy by exploiting interpatterns similarity/dissimilarity. To demonstrate the capabilities of our proposal we present an SNN instance tailored for low resolution MNIST handwritten digits recognition and evaluate its recognition accuracy by means of SPICE simulations. 2 voltage levels are initially utilized for synaptic weight values representation and the recognition accuracy varies from 75.8% to 99.2%, which, together with its compactness and energy efficient (pJ range/spike), suggests that our approach has great potential for edge device implementations.",

keywords = "GNR, Graphene, Nanoelectronics, Neuromorphic Computing, Pattern Recognition, Spiking Neural Network",

author = "Laurencin, {Nicoleta Cucu} and Charles Timmermans and Cotofana, {Sorin D.}",

note = "Green Open Access added to TU Delft Institutional Repository {\textquoteleft}You share, we take care!{\textquoteright} – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. ; 2024 IEEE International Symposium on Circuits and Systems, ISCAS 2024 ; Conference date: 19-05-2024 Through 22-05-2024",

year = "2024",

doi = "10.1109/ISCAS58744.2024.10558243",

language = "English",

series = "Proceedings - IEEE International Symposium on Circuits and Systems",

publisher = "IEEE",

booktitle = "ISCAS 2024 - IEEE International Symposium on Circuits and Systems",

address = "United States",

}

Laurencin, NC, Timmermans, C & Cotofana, SD 2024, An Energy-Efficient Graphene-based Spiking Neural Network Architecture for Pattern Recognition. in ISCAS 2024 - IEEE International Symposium on Circuits and Systems. Proceedings - IEEE International Symposium on Circuits and Systems, IEEE, 2024 IEEE International Symposium on Circuits and Systems, ISCAS 2024, Singapore, Singapore, 19/05/24. https://doi.org/10.1109/ISCAS58744.2024.10558243

An Energy-Efficient Graphene-based Spiking Neural Network Architecture for Pattern Recognition. / Laurencin, Nicoleta Cucu; Timmermans, Charles; Cotofana, Sorin D.
ISCAS 2024 - IEEE International Symposium on Circuits and Systems. IEEE, 2024. (Proceedings - IEEE International Symposium on Circuits and Systems).

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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AU - Laurencin, Nicoleta Cucu

AU - Timmermans, Charles

AU - Cotofana, Sorin D.

N1 - Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

PY - 2024

Y1 - 2024

N2 - In this paper we propose a generic graphene-based Spiking Neural Network (SNN) architecture for pattern recognition and the associated weight values initialization methodology. The SNN has a Winner-Takes-All 3-layer structure and exhibits tuneable recognition accuracy by exploiting interpatterns similarity/dissimilarity. To demonstrate the capabilities of our proposal we present an SNN instance tailored for low resolution MNIST handwritten digits recognition and evaluate its recognition accuracy by means of SPICE simulations. 2 voltage levels are initially utilized for synaptic weight values representation and the recognition accuracy varies from 75.8% to 99.2%, which, together with its compactness and energy efficient (pJ range/spike), suggests that our approach has great potential for edge device implementations.

AB - In this paper we propose a generic graphene-based Spiking Neural Network (SNN) architecture for pattern recognition and the associated weight values initialization methodology. The SNN has a Winner-Takes-All 3-layer structure and exhibits tuneable recognition accuracy by exploiting interpatterns similarity/dissimilarity. To demonstrate the capabilities of our proposal we present an SNN instance tailored for low resolution MNIST handwritten digits recognition and evaluate its recognition accuracy by means of SPICE simulations. 2 voltage levels are initially utilized for synaptic weight values representation and the recognition accuracy varies from 75.8% to 99.2%, which, together with its compactness and energy efficient (pJ range/spike), suggests that our approach has great potential for edge device implementations.

KW - GNR

KW - Graphene

KW - Nanoelectronics

KW - Neuromorphic Computing

KW - Pattern Recognition

KW - Spiking Neural Network

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M3 - Conference contribution

AN - SCOPUS:85198514946

T3 - Proceedings - IEEE International Symposium on Circuits and Systems

BT - ISCAS 2024 - IEEE International Symposium on Circuits and Systems

PB - IEEE

T2 - 2024 IEEE International Symposium on Circuits and Systems, ISCAS 2024

Y2 - 19 May 2024 through 22 May 2024

ER -

An Energy-Efficient Graphene-based Spiking Neural Network Architecture for Pattern Recognition

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Keywords

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