Toward sub-pJ per classification in Body Area Sensor Networks

Paul Chollet; Kevin Colombier; Cyril Lahuec; Matthieu Arzel; Fabrice Seguin

doi:10.1109/NEWCAS.2016.7604764

Toward sub-pJ per classification in Body Area Sensor Networks

Paul Chollet
, Kevin Colombier
, Cyril Lahuec
, Matthieu Arzel
, Fabrice Seguin

CNRS

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

Résumé

Body Area Sensor Networks (BASN) are expected to provide a way to improve medical care while reducing its costs. Reducing their energy consumption is a critical step before building reliable and durable systems. Acquisition and classification of Electrocardiogram (ECG) signals is a central task in medical BASNs. This paper introduces a method to perform the classification between three abnormal types of heart beats at ultra-low power using Sparse Neural Associative Memories (SNAM). Based on recent analog implementation of a SNAM node using the ST CMOS 65 nm design kit, the proposed SNAM uses only 864 fJ per classification. Compared to a digital ultra-low power multi-core architecture, this SNAM consumes several orders of magnitude less energy while achieving classification accuracy of 93.5 %.

langue originale	Anglais
titre	14th IEEE International NEWCAS Conference, NEWCAS 2016
Editeur	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronique)	9781467389006
Les DOIs	https://doi.org/10.1109/NEWCAS.2016.7604764
état	Publié - 20 oct. 2016
Evénement	14th IEEE International NEWCAS Conference, NEWCAS 2016 - Vancouver, Canada Durée: 26 juin 2016 → 29 juin 2016

Série de publications

Nom	14th IEEE International NEWCAS Conference, NEWCAS 2016

Une conférence

Une conférence	14th IEEE International NEWCAS Conference, NEWCAS 2016
Pays/Territoire	Canada
La ville	Vancouver
période	26/06/16 → 29/06/16

SDG des Nations Unies

Ce résultat contribue à ou aux Objectifs de développement durable suivants

SDG 7 Énergie abordable et propre

Accès au document

10.1109/NEWCAS.2016.7604764

Contient cette citation

@inproceedings{05a6e0b9e622480f8b54969d2ea2241c,

title = "Toward sub-pJ per classification in Body Area Sensor Networks",

abstract = "Body Area Sensor Networks (BASN) are expected to provide a way to improve medical care while reducing its costs. Reducing their energy consumption is a critical step before building reliable and durable systems. Acquisition and classification of Electrocardiogram (ECG) signals is a central task in medical BASNs. This paper introduces a method to perform the classification between three abnormal types of heart beats at ultra-low power using Sparse Neural Associative Memories (SNAM). Based on recent analog implementation of a SNAM node using the ST CMOS 65 nm design kit, the proposed SNAM uses only 864 fJ per classification. Compared to a digital ultra-low power multi-core architecture, this SNAM consumes several orders of magnitude less energy while achieving classification accuracy of 93.5 \%.",

author = "Paul Chollet and Kevin Colombier and Cyril Lahuec and Matthieu Arzel and Fabrice Seguin",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 14th IEEE International NEWCAS Conference, NEWCAS 2016 ; Conference date: 26-06-2016 Through 29-06-2016",

year = "2016",

month = oct,

day = "20",

doi = "10.1109/NEWCAS.2016.7604764",

language = "English",

series = "14th IEEE International NEWCAS Conference, NEWCAS 2016",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "14th IEEE International NEWCAS Conference, NEWCAS 2016",

}

Chollet, P, Colombier, K, Lahuec, C, Arzel, M & Seguin, F 2016, Toward sub-pJ per classification in Body Area Sensor Networks. Dans 14th IEEE International NEWCAS Conference, NEWCAS 2016., 7604764, 14th IEEE International NEWCAS Conference, NEWCAS 2016, Institute of Electrical and Electronics Engineers Inc., 14th IEEE International NEWCAS Conference, NEWCAS 2016, Vancouver, Canada, 26/06/16. https://doi.org/10.1109/NEWCAS.2016.7604764

Toward sub-pJ per classification in Body Area Sensor Networks. / Chollet, Paul; Colombier, Kevin; Lahuec, Cyril et al.
14th IEEE International NEWCAS Conference, NEWCAS 2016. Institute of Electrical and Electronics Engineers Inc., 2016. 7604764 (14th IEEE International NEWCAS Conference, NEWCAS 2016).

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

TY - GEN

T1 - Toward sub-pJ per classification in Body Area Sensor Networks

AU - Chollet, Paul

AU - Colombier, Kevin

AU - Lahuec, Cyril

AU - Arzel, Matthieu

AU - Seguin, Fabrice

PY - 2016/10/20

Y1 - 2016/10/20

N2 - Body Area Sensor Networks (BASN) are expected to provide a way to improve medical care while reducing its costs. Reducing their energy consumption is a critical step before building reliable and durable systems. Acquisition and classification of Electrocardiogram (ECG) signals is a central task in medical BASNs. This paper introduces a method to perform the classification between three abnormal types of heart beats at ultra-low power using Sparse Neural Associative Memories (SNAM). Based on recent analog implementation of a SNAM node using the ST CMOS 65 nm design kit, the proposed SNAM uses only 864 fJ per classification. Compared to a digital ultra-low power multi-core architecture, this SNAM consumes several orders of magnitude less energy while achieving classification accuracy of 93.5 %.

AB - Body Area Sensor Networks (BASN) are expected to provide a way to improve medical care while reducing its costs. Reducing their energy consumption is a critical step before building reliable and durable systems. Acquisition and classification of Electrocardiogram (ECG) signals is a central task in medical BASNs. This paper introduces a method to perform the classification between three abnormal types of heart beats at ultra-low power using Sparse Neural Associative Memories (SNAM). Based on recent analog implementation of a SNAM node using the ST CMOS 65 nm design kit, the proposed SNAM uses only 864 fJ per classification. Compared to a digital ultra-low power multi-core architecture, this SNAM consumes several orders of magnitude less energy while achieving classification accuracy of 93.5 %.

U2 - 10.1109/NEWCAS.2016.7604764

DO - 10.1109/NEWCAS.2016.7604764

M3 - Conference contribution

AN - SCOPUS:84998534542

T3 - 14th IEEE International NEWCAS Conference, NEWCAS 2016

BT - 14th IEEE International NEWCAS Conference, NEWCAS 2016

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 14th IEEE International NEWCAS Conference, NEWCAS 2016

Y2 - 26 June 2016 through 29 June 2016

ER -

Toward sub-pJ per classification in Body Area Sensor Networks

Résumé

Série de publications

Une conférence

SDG des Nations Unies

Accès au document

Empreinte digitale

Contient cette citation