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

Authors: de Bruin, B. and Singh, K. and Huisken, J. and Corporaal, H.

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 28nm FDSOI technology, 325uJ per classification at 0.9V and 290uJ at 0.5V are achieved using an optimized software-only implementation. By leveraging a Coarse-Grained Reconfigurable Array (CGRA), 160uJ and 120uJ 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 55%, including idle-time overhead.

[BibTeX]
@inproceedings{DeBruin,
  author = {de Bruin, B. and Singh, K. and Huisken, J. and Corporaal, H.},
  booktitle = {IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED)},
  isbn = {9781450370530},
  keywords = {edge processing,energy-efficiency,offs,reconfigurable accelerators,system-level trade-,wearable eeg monitoring},
  title = {{BrainWave: an energy-efficient EEG monitoring system - evaluation and trade-offs}},
  year = {2020},
  volume = {},
  number = {},
  pages = {xx}
}