Towards energy-efficient parallel analysis of neural signals

Authors:
Dan Chen;Dongcuan Lu;Mingwei Tian;Shan He;Shuaiting Wang;Jian Tian;Chang Cai;Xiaoli Li
Affiliations:
School of Computer Science, China University of Geosciences, Wuhan, China 430074 and School of Computer Science, University of Birmingham, Birmingham, UK B15 2TT;Institute of Electrical Engineering, Yanshan University, Qinhuangdao, China 066004;Institute of Electrical Engineering, Yanshan University, Qinhuangdao, China 066004;School of Computer Science, University of Birmingham, Birmingham, UK B15 2TT;Institute of Electrical Engineering, Yanshan University, Qinhuangdao, China 066004;School of Economics, Huazhong University of Science & Technology, Wuhan, China 430074;School of Computer Science, China University of Geosciences, Wuhan, China 430074;Institute of Electrical Engineering, Yanshan University, Qinhuangdao, China 066004
Venue:
Cluster Computing
Year:
2013

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Abstract

Recent advances of experimental methods and neuroscience research have made neural signals constantly massive and analysis of these signals highly compute-intensive. This study explore the possibility proposes a massively parallel approach for analysis of neural signals using General-purpose computing on the graphics processing unit (GPGPU). We demonstrate the uses and correctness of the proposed approach via a case of analyzing EEG with focal epilepsy. An experimental examination has been carried out to investigate (1) the GPGPU-aided approach's performance and (2) energy costs of the GPGPU-aided application versus the original CPU-only systems. Experimental results indicate that the proposed approach excels in both aspects.