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An Energy-Efficient Biomedical Signal Processing Platform

descriptionPublicationkeyboard_double_arrow_right Article 01 Sep 2010 United States Publisher:Institute of Electrical and Electronics Engineers (IEEE)Journal:IEEE Journal of Solid-State Circuits, volume 46, pages 1,742-1,753 (issn: 0018-9200, eissn: 1558-173X,

Authors: Kwong, Joyce; Chandrakasan, Anantha P.;

doi: 10.1109/jssc.2011.2144450 , 10.1109/esscirc.2010.5619759

handle: 1721.1/72195

An Energy-Efficient Biomedical Signal Processing Platform

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Abstract

This paper presents an energy-efficient processing platform for wearable sensor nodes, designed to support diverse biological signals and algorithms. The platform features a 0.5V-1.0V 16b microcontroller, SRAM, and accelerators for biomedical signal processing. Voltage scaling and block-level power gating allow optimizing energy efficiency under applications of varying complexity. Programmable accelerators support numerous usage scenarios and perform signal processing tasks at 133 to 215× lower energy than the general-purpose CPU. When running complete EEG and EKG applications using both CPU and accelerators, the platform achieves 10.2× and 11.5× energy reduction respectively compared to CPU-only implementations.

Country

United States

Related Organizations

Texas Instruments (United States)
United States
Massachusetts Institute of Technology
United States

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	106
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 10%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 1%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 10%