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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.
|Title of host publication||Proceedings of the ACM/IEEE International Symposium on Low Power Electronics and Design, ISLPED 2020|
|Subtitle of host publication||ISLPED '20|
|Number of pages||6|
|Publication status||Published - 10 Aug 2020|
|Name||ACM International Conference Proceeding Series|
- edge processing
- reconfigurable accelerators
- system-level trade-offs
- wearable EEG monitoring
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