Rakeness-based compressed sensing on ultra-low power multi-core biomedicai processors

Daniele Bortolotti; Mauro Mangia; Andrea Bartolini; Riccardo Rovatti; Gianluca Setti; Luca Benini

doi:10.1109/DASIP.2014.7115599

Rakeness-based compressed sensing on ultra-low power multi-core biomedicai processors

Daniele Bortolotti, Mauro Mangia, Andrea Bartolini, Riccardo Rovatti, Gianluca Setti, Luca Benini

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

8 Scopus citations

Abstract

Technology scaling enables today the design of ultra-low cost wireless body sensor networks for wearable biomedical monitors. The typical behaviour of such systems consists of multi-channel input biosignals acquisition data compression and final output transmission or storage. To achieve minimal energy operation and extend battery life several aspects must be considered ranging from signal processing to architectural optimizations. The recently proposed Rakeness-based Compressed Sensing (CS) paradigm deploys the localization of input signal energy to further increase compression without sensible RSNR degradation. Such output size reduction allows for trading off energy from the compression stage to the transmission or storage stage. In this paper we analyze such tradeoffs considering a multi-core DSP for input biosignal computation and different technologies for either transmission or local storage. The experimental results show the effectiveness of the Rakeness approach (on average ≈ 44% more efficient than the baseline) and assess the energy gains in a technological perspective.

Original language	English (US)
Title of host publication	Conference on Design and Architectures for Signal and Image Processing, DASIP
Publisher	IEEE Computer Society
ISBN (Print)	9791092279054
DOIs	https://doi.org/10.1109/DASIP.2014.7115599
State	Published - May 29 2015
Externally published	Yes

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10.1109/DASIP.2014.7115599

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title = "Rakeness-based compressed sensing on ultra-low power multi-core biomedicai processors",

abstract = "Technology scaling enables today the design of ultra-low cost wireless body sensor networks for wearable biomedical monitors. The typical behaviour of such systems consists of multi-channel input biosignals acquisition data compression and final output transmission or storage. To achieve minimal energy operation and extend battery life several aspects must be considered ranging from signal processing to architectural optimizations. The recently proposed Rakeness-based Compressed Sensing (CS) paradigm deploys the localization of input signal energy to further increase compression without sensible RSNR degradation. Such output size reduction allows for trading off energy from the compression stage to the transmission or storage stage. In this paper we analyze such tradeoffs considering a multi-core DSP for input biosignal computation and different technologies for either transmission or local storage. The experimental results show the effectiveness of the Rakeness approach (on average ≈ 44% more efficient than the baseline) and assess the energy gains in a technological perspective.",

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AU - Bortolotti, Daniele

AU - Mangia, Mauro

AU - Bartolini, Andrea

AU - Rovatti, Riccardo

AU - Setti, Gianluca

AU - Benini, Luca

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N2 - Technology scaling enables today the design of ultra-low cost wireless body sensor networks for wearable biomedical monitors. The typical behaviour of such systems consists of multi-channel input biosignals acquisition data compression and final output transmission or storage. To achieve minimal energy operation and extend battery life several aspects must be considered ranging from signal processing to architectural optimizations. The recently proposed Rakeness-based Compressed Sensing (CS) paradigm deploys the localization of input signal energy to further increase compression without sensible RSNR degradation. Such output size reduction allows for trading off energy from the compression stage to the transmission or storage stage. In this paper we analyze such tradeoffs considering a multi-core DSP for input biosignal computation and different technologies for either transmission or local storage. The experimental results show the effectiveness of the Rakeness approach (on average ≈ 44% more efficient than the baseline) and assess the energy gains in a technological perspective.

AB - Technology scaling enables today the design of ultra-low cost wireless body sensor networks for wearable biomedical monitors. The typical behaviour of such systems consists of multi-channel input biosignals acquisition data compression and final output transmission or storage. To achieve minimal energy operation and extend battery life several aspects must be considered ranging from signal processing to architectural optimizations. The recently proposed Rakeness-based Compressed Sensing (CS) paradigm deploys the localization of input signal energy to further increase compression without sensible RSNR degradation. Such output size reduction allows for trading off energy from the compression stage to the transmission or storage stage. In this paper we analyze such tradeoffs considering a multi-core DSP for input biosignal computation and different technologies for either transmission or local storage. The experimental results show the effectiveness of the Rakeness approach (on average ≈ 44% more efficient than the baseline) and assess the energy gains in a technological perspective.

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BT - Conference on Design and Architectures for Signal and Image Processing, DASIP

PB - IEEE Computer Society

ER -

Rakeness-based compressed sensing on ultra-low power multi-core biomedicai processors

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