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Learning-Based Near-Optimal Area-Power Trade-offs in Hardware Design for Neural Signal Acquisition

Aprile, Cosimo and Baldassarre, Luca and Gupta, Vipul and Yoo, Juhwan and Shoaran, Mahsa and Leblebici, Yusuf and Cevher, Volkan (2016) Learning-Based Near-Optimal Area-Power Trade-offs in Hardware Design for Neural Signal Acquisition. In: Proceedings of the 26th edition on Great Lakes Symposium on VLSI. Association for Computing Machinery , New York, NY, pp. 433-438. ISBN 978-1-4503-4274-2. https://resolver.caltech.edu/CaltechAUTHORS:20160824-123308419

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Abstract

Wireless implantable devices capable of monitoring the electrical activity of the brain are becoming an important tool for understanding and potentially treating mental diseases such as epilepsy and depression. While such devices exist, it is still necessary to address several challenges to make them more practical in terms of area and power dissipation. In this work, we apply Learning Based Compressive Subsampling (LBCS) to tackle the power and area trade-offs in neural wireless devices. To this end, we propose a low-power and area-efficient system for neural signal acquisition which yields state-of-art compression rates up to 64x with high reconstruction quality, as demonstrated on two human iEEG datasets. This new fully digital architecture handles one neural acquisition channel, with an area of 210 x 210 μm in 90nm CMOS technology, and a power dissipation of only 1μW.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1145/2902961.2903028 DOIArticle
http://dl.acm.org/citation.cfm?doid=2902961.2903028PublisherArticle
Additional Information:© 2016 ACM. This work was supported in part by the European Commission under grant ERC Future Proof and by the Swiss Science Foundation under grants SNF 200021-146750 and SNF CRSII2-147633. The authors would like to thank Jonathan Narinx for useful discussions on the system design.
Funders:
Funding AgencyGrant Number
European Research Council (ERC)UNSPECIFIED
Swiss Science Foundation (SNF)SNF 200021-146750
Swiss Science Foundation (SNF)CRSII2-147633
Subject Keywords:Neural signals, Compressive Sensing, digital signal processing, area-efficient, low-power, signal recovery
Record Number:CaltechAUTHORS:20160824-123308419
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20160824-123308419
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:69903
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:24 Aug 2016 20:47
Last Modified:03 Oct 2019 10:26

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