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Communication Dans Un Congrès Année : 2016

Toward sub-pJ per classification in Body Area Sensor Networks

Paul Chollet
Kevin Colombier
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  • PersonId : 1004501
Cyril Lahuec

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 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 %.
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Dates et versions

hal-01493883 , version 1 (22-03-2017)

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Paul Chollet, Kevin Colombier, Cyril Lahuec, Matthieu Arzel, Fabrice Seguin. Toward sub-pJ per classification in Body Area Sensor Networks. NEWCAS 2016 : 14th IEEE International on New Circuits and Systems, Jun 2016, Vancouver, Canada. pp.1 - 4, ⟨10.1109/NEWCAS.2016.7604764⟩. ⟨hal-01493883⟩
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