標題: Energy-Efficient FastICA Implementation for Biomedical Signal Separation
作者: Lan-Da Van
Wu, Di-You
Chen, Chien-Shiun
資訊工程學系
Department of Computer Science
關鍵字: Blind source separation;electroencephalogram;energy efficiency;fast independent component analysis;hardware implementation
公開日期: 1-Nov-2011
摘要: This paper presents an energy-efficient fast independent component analysis (FastICA) implementation with an early determination scheme for eight-channel electroencephalogram (EEG) signal separation. The main contributions are as follows: 1) energy-efficient FastICA using the proposed early determination scheme and the corresponding architecture; 2) cost-effective FastICA using the proposed preprocessing unit architecture with one coordinate rotation digital computer-based eigenvalue decomposition processor and the proposed one-unit architecture with the hardware reuse scheme; and 3) low-computation-time FastICA using the four parallel one-units architecture. The resulting power dissipation of the FastICA implementation for eight-channel EEG signal separation is 16.35 mW at 100 MHz at 1.0 V. Compared with the design without early determination, the proposed FastICA architecture implemented in united microelectronics corporation 90 nm 1P9M complementary metal-oxide-semiconductor process with a core area of 1.221 x 1.218 mm(2) can achieve average energy reduction by 47.63%. From the post-layout simulation results, the maximum computation time is 0.29 s.
URI: http://dx.doi.org/10.1109/TNN.2011.2166979
http://hdl.handle.net/11536/14697
ISSN: 1045-9227
DOI: 10.1109/TNN.2011.2166979
期刊: IEEE TRANSACTIONS ON NEURAL NETWORKS
Volume: 22
Issue: 11
起始頁: 1809
結束頁: 1822
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