TY - GEN
T1 - Neuronal connectivity assessment for epileptic seizure prevention
T2 - 14th International Conference on Embedded Computer Systems: Architectures, Modeling and Simulation, SAMOS 2014
AU - Georgis, Georgios
AU - Reisis, Dionysios
AU - Skordilakis, Panagiotis
AU - Tsakalis, Konstantinos
AU - Shafique, Ashfaque Bin
AU - Chatzikonstantis, George
AU - Lentaris, George
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014
Y1 - 2014
N2 - Research on the prevention of epileptic seizures has led to approaches for future treatment techniques, which rely on the demanding computation of generalized partial directed coherence (GPDC) on electroencephalogram (EEG) data. A fast computation of such metrics is a key factor both for the off-line optimization of algorithmic parameters and for its real-time implementation. Aiming at speeding up the GPDC computations on EEG data, the current paper presents massively parallel computational strategies for implementing the GPDC on many-core architectures. We apply the proposed strategies on commercial and experimental many-core platforms and we compare the results of the computation time of a set of EEG data on the Bulldozer and Ivy Bridge x86-64 serial processors. We test the GPUs of nVidia GTX 550 Ti and GTX 670, which at the best case achieve a significant speedup of 190x and 460x respectively. Moreover, we apply the proposed parallelization strategies on the Single-Chip Cloud Computer (SCC), an experimental processor created by Intel Labs.
AB - Research on the prevention of epileptic seizures has led to approaches for future treatment techniques, which rely on the demanding computation of generalized partial directed coherence (GPDC) on electroencephalogram (EEG) data. A fast computation of such metrics is a key factor both for the off-line optimization of algorithmic parameters and for its real-time implementation. Aiming at speeding up the GPDC computations on EEG data, the current paper presents massively parallel computational strategies for implementing the GPDC on many-core architectures. We apply the proposed strategies on commercial and experimental many-core platforms and we compare the results of the computation time of a set of EEG data on the Bulldozer and Ivy Bridge x86-64 serial processors. We test the GPUs of nVidia GTX 550 Ti and GTX 670, which at the best case achieve a significant speedup of 190x and 460x respectively. Moreover, we apply the proposed parallelization strategies on the Single-Chip Cloud Computer (SCC), an experimental processor created by Intel Labs.
UR - http://www.scopus.com/inward/record.url?scp=84907901189&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84907901189&partnerID=8YFLogxK
U2 - 10.1109/SAMOS.2014.6893234
DO - 10.1109/SAMOS.2014.6893234
M3 - Conference contribution
AN - SCOPUS:84907901189
T3 - Proceedings - International Conference on Embedded Computer Systems: Architectures, Modeling and Simulation, SAMOS 2014
SP - 359
EP - 366
BT - Proceedings - International Conference on Embedded Computer Systems
A2 - Veidenbaum, Alexander V.
A2 - Galuzzi, Carlo
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 14 July 2014 through 17 July 2014
ER -