ECG authentication neural network hardware design with collective optimization of low precision and structured compression

Sai Kiran Cherupally, Gaurav Srivastava, Shihui Yin, Deepak Kadetotad, Chisung Bae, Sang Joon Kim, Seo Jae-Sun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

For wearable devices that monitor personal health, secure access to private medical data becomes a crucial feature. Nowadays, device authentication based on biometrics such as fingerprint or iris has become increasingly popular. In this work, we investigate using electrocardiogram (ECG) signals as the biometric modality for device authentication, and we present accurate and low-power ECG-based authentication hardware. Deep neural networks (DNNs) have been employed with a cost function that maximizes inter-individual distance and minimizes intra-individual distance over time. During DNN training, we also introduce joint optimization of low-precision and structured sparsity, so that the real-time authentication hardware can consume minimal energy and area. Experimental results of custom hardware designed in 65nm LP CMOS technology exhibit low power consumption of 59.4 µW for real-time ECG authentication with a low equal error rate of 1.002% for a large 741-subject in-house ECG database.

Original languageEnglish (US)
Title of host publication2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728103976
DOIs
StatePublished - Jan 1 2019
Event2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Sapporo, Japan
Duration: May 26 2019May 29 2019

Publication series

NameProceedings - IEEE International Symposium on Circuits and Systems
Volume2019-May
ISSN (Print)0271-4310

Conference

Conference2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019
CountryJapan
CitySapporo
Period5/26/195/29/19

Keywords

  • Authentication
  • Deep neural network
  • ECG
  • Low-power hardware
  • Structural sparsity

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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    Cherupally, S. K., Srivastava, G., Yin, S., Kadetotad, D., Bae, C., Kim, S. J., & Jae-Sun, S. (2019). ECG authentication neural network hardware design with collective optimization of low precision and structured compression. In 2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Proceedings [8702308] (Proceedings - IEEE International Symposium on Circuits and Systems; Vol. 2019-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISCAS.2019.8702308