Value locality based storage compression memory architecture for ECG sensor node

This paper proposes a value compression memory architecture for QRS detection in ultra-low-power ECG sensor nodes. Based on the exploration of value spatial locality in the most critical preprocessing stage of the ECG algorithm, a cost efficient compression strategy, which reorganizes several adjace...

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Veröffentlicht in:Science China. Information sciences 2016-04, Vol.59 (4), p.41-51, Article 042401
Hauptverfasser: Zhao, Chaojun, Chen, Chen, Chen, Zhijian, Meng, Jianyi
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Sprache:eng
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Zusammenfassung:This paper proposes a value compression memory architecture for QRS detection in ultra-low-power ECG sensor nodes. Based on the exploration of value spatial locality in the most critical preprocessing stage of the ECG algorithm, a cost efficient compression strategy, which reorganizes several adjacent sample values into a base value with several displacements, is proposed. The displacements will be half or quarter scale quantifications; as a result, the storage size is reduced. The memory architecture saves memory space by storing compressed data with value spatial locality into a compressed memory section and by using a small, uncompressed memory section as backup to store the uncompressed data when a value spatial locality miss occurs. Furthermore,a low-power accession strategy is proposed to achieve low-power accession. An embodiment of the proposed memory architecture has been evaluated using the MIT/BIH database, the proposed memory architecture and a low-power accession strategy to achieve memory space savings of 32.5% and to achieve a 68.1% power reduction with a negligible performance reduction of 0.2%.
ISSN:1674-733X
1869-1919
DOI:10.1007/s11432-015-5371-1