Hardware Acceleration of High Sensitivity Power-Aware Epileptic Seizure Detection System Using Dynamic Partial Reconfiguration
Zakhari, Michael H.
Elgammal, Mohamed A.
Kelany, Khaled A. Helal
Ghany, Mohamed A. Abd El
Salama, Khaled N.
KAUST DepartmentElectrical Engineering Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Permanent link to this recordhttp://hdl.handle.net/10754/669517
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AbstractIn this paper, a high-sensitivity low-cost power-aware Support Vector Machine (SVM) training and classification based system, is hardware implemented for a neural seizure detection application. The training accelerator algorithm, adopted in this work, is the sequential minimal optimization (SMO). System blocks are implemented to achieve the best trade-off between sensitivity and the consumption of area and power. The proposed seizure detection system achieves 98.38% sensitivity when tested with the implemented linear kernel classifier. The system is implemented on different platforms: such as Field Programmable Gate Array (FPGA) Xilinx Virtex-7 board and Application Specific Integrated Circuit (ASIC) using hardware-calibrated UMC 65nm CMOS technology. A power consumption evaluation is performed on both the ASIC and FPGA platforms showing that the ASIC power consumption is lower by at least 65% when compared with the FPGA counterpart. A power-aware system is implemented with FPGAs by the adoption of the Dynamic Partial Reconfiguration (DPR) technique that allows the dynamic operation of the system based on power level available to the system at the expense of degradation of the system accuracy. The proposed system exploits the advantages of DPR technology in FPGAs to switch between two proposed designs providing a decrease of 64% in power consumption.
CitationElhosary, H., Zakhari, M. H., Elgammal, M. A., Helal Kelany, K. A., Abd El Ghany, M. A., Salama, K. N., & Mostafa, H. (2021). Hardware Acceleration of High Sensitivity Power-Aware Epileptic Seizure Detection System Using Dynamic Partial Reconfiguration. IEEE Access, 1–1. doi:10.1109/access.2021.3079155
SponsorsThis work was supported in part by the Opto-Nano-Electronics (ONE) Lab at Zewail City of Science and Technology and at Cairo University, in part by the Information Technology Industry Development Agency (ITIDA), and in part by the Academy of Scientific Research and Technology (ASRT).
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