Energy efficient processing architectures represent key elements for wearable and implantable medical devices. Signal processing of neural data is a challenge in new designs of Brain Machine Interfaces (BMI). A highly efficient multi-core platform, designed for ultra low power processing allows the execution of complex algorithms complying with real time requirements. This paper describes the implementation and optimization of a seizure detection algorithm on a multi-core digital integrated circuit designed for energy efficient applications. The proposed architecture is able to implement ultra low power parallel processing seizure detection on 23 electrodes within a power budget of 1 mW, outperforming implementations on commercial MCUs by up to 100 times in terms of performance and up to 80 times in terms of energy efficiency still providing high versatility and scalability, opening the way to the development of efficient implantable and wearable smart systems.
Scalable EEG seizure detection on an ultra low power multi-core architecture / Benatti, S.; Montagna, F.; Rossi, D.; Benini, L.. - (2016), pp. 86-89. (Intervento presentato al convegno 12th IEEE Biomedical Circuits and Systems Conference, BioCAS 2016 tenutosi a chn nel 2016) [10.1109/BioCAS.2016.7833731].
Scalable EEG seizure detection on an ultra low power multi-core architecture
Benatti S.;
2016
Abstract
Energy efficient processing architectures represent key elements for wearable and implantable medical devices. Signal processing of neural data is a challenge in new designs of Brain Machine Interfaces (BMI). A highly efficient multi-core platform, designed for ultra low power processing allows the execution of complex algorithms complying with real time requirements. This paper describes the implementation and optimization of a seizure detection algorithm on a multi-core digital integrated circuit designed for energy efficient applications. The proposed architecture is able to implement ultra low power parallel processing seizure detection on 23 electrodes within a power budget of 1 mW, outperforming implementations on commercial MCUs by up to 100 times in terms of performance and up to 80 times in terms of energy efficiency still providing high versatility and scalability, opening the way to the development of efficient implantable and wearable smart systems.
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