ECG Authentication Hardware Design with Low-Power Signal Processing and Neural Network Optimization with Low Precision and Structured Compression

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

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Biometrics such as facial features, fingerprint, and iris are being used increasingly in modern authentication systems. These methods are now popular and have found their way into many portable electronics such as smartphones, tablets, and laptops. Furthermore, the use of biometrics enables secure access to private medical data, now collected in wearable devices such as smartwatches. In this work, we present an accurate low-power device authentication system that employs electrocardiogram (ECG) signals as the biometric modality. The proposed ECG processor consists of front-end signal processing of ECG signals and back-end neural networks (NNs) for accurate authentication. The NNs are trained using a cost function that minimizes intra-individual distance over time and maximizes inter-individual distance. Efficient low-power hardware was implemented by using fixed coefficients for ECG signal pre-processing and by using joint optimization of low-precision and structured sparsity for the NNs. We implemented two instances of ECG authentication hardware with 4X and 8X structurally-compressed NNs in 65 nm LP CMOS, which consume low power of 62.37 μW and 75.41 μW for real-time ECG authentication with a low equal error rate of 1.36% and 1.21%, respectively, for a large 741-subject in-house ECG database. The hardware was evaluated at 10 kHz clock frequency and 1.2 V voltage supply.

Original languageEnglish (US)
Article number9000713
Pages (from-to)198-208
Number of pages11
JournalIEEE transactions on biomedical circuits and systems
Volume14
Issue number2
DOIs
StatePublished - Apr 2020

Keywords

  • DNN compression
  • ECG authentication
  • fixed-coefficient filtering
  • neural network optimization
  • structured sparsity

ASJC Scopus subject areas

  • Biomedical Engineering
  • Electrical and Electronic Engineering

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