Overcoming Computational Errors in Sensing Platforms Through Embedded Machine-Learning Kernels

Zhuo Wang, Kyong Ho Lee, Naveen Verma

Research output: Contribution to journalArticle

19 Scopus citations

Abstract

We present an approach for overcoming computational errors at run time that originate from static hardware faults in digital processors. The approach is based on embedded machine-learning stages that learn and model the statistics of the computational outputs in the presence of errors, resulting in an error-aware model for embedded analysis. We demonstrate, in hardware, two systems for analyzing sensor data: 1) an EEG-based seizure detector and 2) an ECG-based cardiac arrhythmia detector. The systems use a small kernel of fault-free hardware (constituting <7.0% and <31% of the total areas respectively) to construct and apply the error-aware model. The systems construct their own error-aware models with minimal overhead through the use of an embedded active-learning framework. Via an field-programmable gate array implementation for hardware experiments, stuck-at faults are injected at controllable rates within synthesized gate-level netlists to permit characterization. The seizure detector demonstrates restored performance despite faults on 0.018% of the circuit nodes [causing bit error rates (BERs) up to 45%], and the arrhythmia detector demonstrates restored performance despite faults on 2.7% of the circuit nodes (causing BERs up to 50%).

Original languageEnglish (US)
Article number6874569
Pages (from-to)1459-1470
Number of pages12
JournalIEEE Transactions on Very Large Scale Integration (VLSI) Systems
Volume23
Issue number8
DOIs
StatePublished - Aug 1 2015

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Electrical and Electronic Engineering

Keywords

  • Embedded sensing
  • fault tolerance
  • hardware resiliency
  • machine learning
  • run-time error correction

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