BrainWave: an energy-efficient EEG monitoring system - evaluation and trade-offs

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

1 Citation (Scopus)
240 Downloads (Pure)

Abstract

This paper presents the design and evaluation of an energy-efficient seizure detection system for emerging EEG-based monitoring applications, such as non-convulsive epileptic seizure detection and Freezing-of-Gait (FoG) detection. As part of the BrainWave system, a BrainWave processor for flexible and energy-efficient signal processing is designed. The key system design parameters, including algorithmic optimizations, feature offloading and near-Threshold computing are evaluated in this work. The BrainWave processor is evaluated while executing a complex EEG-based epileptic seizure detection algorithm. In a 28-nm FDSOI technology, 325 μJ per classification at 0.9 V and 290 μJ at 0.5 V are achieved using an optimized software-only implementation. By leveraging a Coarse-Grained Reconfigurable Array (CGRA), 160 μJ and 135 μJ are obtained, respectively, while maintaining a high level of flexibility. Near-Threshold computing combined with CGRA acceleration leads to an energy reduction of up to 59%, or 55% including idle-Time overhead.

Original languageEnglish
Title of host publicationProceedings of the ACM/IEEE International Symposium on Low Power Electronics and Design, ISLPED 2020
Subtitle of host publicationISLPED '20
PublisherACM/IEEE
Pages181–186
Number of pages6
ISBN (Electronic)9781450370530
DOIs
Publication statusPublished - 10 Aug 2020

Publication series

NameACM International Conference Proceeding Series

Keywords

  • edge processing
  • energy-efficiency
  • reconfigurable accelerators
  • system-level trade-offs
  • wearable EEG monitoring

Fingerprint

Dive into the research topics of 'BrainWave: an energy-efficient EEG monitoring system - evaluation and trade-offs'. Together they form a unique fingerprint.

Cite this