title: Energy-efficient FastICA implementation for biomedical signal separation.
abstract: 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 × 1.218 mm2 can achieve average energy reduction by 47.63%. From the post-layout simulation results, the maximum computation time is 0.29 s.
authors.affiliation: Department of Computer Science, National Chiao Tung University, Hsinchu 300, Taiwan. ldvan@cs.nctu.edu.tw
authors.forename: Lan-Da
authors.initials: LD
authors.lastname: Van
authors.forename: Di-You
authors.initials: DY
authors.lastname: Wu
authors.forename: Chien-Shiun
authors.initials: CS
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D020811:Biomedical Technology / Q000379:methods*; D003198:Computer Simulation; D003199:Computer Systems; D003246:Conservation of Energy Resources; D003627:Data Interpretation, Statistical; D004569:Electroencephalography / Q000706:statistics & numerical data*; D006801:Humans; D025341:Principal Component Analysis / Q000379:methods*; D012815:Signal Processing, Computer-Assisted; D012984:Software