@inproceedings{311cd8ca096b4dc09eaa0849b9b35d70,
title = "XBioSiP: A methodology for approximate bio-signal processing at the edge",
abstract = "Bio-signals exhibit high redundancy, and the algorithms for their processing are inherently error resilient. This property can be leveraged to improve the energy-efficiency of IoT-Edge (wearables) through the emerging trend of approximate computing. This paper presents XBioSiP, a novel methodology for approximate bio-signal processing that employs two quality evaluation stages, during the pre-processing and bio-signal processing stages, to determine the approximation parameters. It thereby achieves high energy savings while satisfying the user-determined quality constraint. Our methodology achieves, up to 19× and 22× reduction in the energy consumption of a QRS peak detection algorithm for 0% and < 1% loss in peak detection accuracy, respectively.",
keywords = "Adders, Approximate Computing, Arithmetic Units, Bio-Signal, ECG, Edge Computing, Energy-Efficiency, Hardware Design, Healthcare, IoT, Multipliers, Wearables",
author = "Prabakaran, {Bharath Srinivas} and Semeen Rehman and Muhammad Shafique",
note = "Publisher Copyright: {\textcopyright} 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.; 56th Annual Design Automation Conference, DAC 2019 ; Conference date: 02-06-2019 Through 06-06-2019",
year = "2019",
month = jun,
day = "2",
doi = "10.1145/3316781.3317933",
language = "English (US)",
series = "Proceedings - Design Automation Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "Proceedings of the 56th Annual Design Automation Conference 2019, DAC 2019",
}