Low complexity hardware accelerator for nD FastICA based on coordinate rotation

This paper proposes a low complex hardware accelerator algorithmic modification for n-dimensional (nD) FastICA methodology based on Coordinate Rotation Digital Computer (CORDIC) to attain high computation speed. The most complex and time consuming update stage and convergence check required for computation of the nth weight vector are eliminated in the proposed methodology. Using the Gram-Schmidt Orthogonalization stage and normalization stage to calculate nth weight vector in an entirely sequential procedure of CORDIC-based FastICA results in a significant gain in terms of the computation time. The proposed methodology has been functionally verified and validated by applying it for separating 6D speech signals. It has been implemented on hardware using Verilog HDL and synthesized using UMC 180nm technology. The average improvement in computation time obtained by using the proposed methodology for 4D to 6D FastICA with 1024 samples, considering the minimum case of two iterations for nth stage, was found to be 98.79 %.

[1]  Bashir M. Al-Hashimi,et al.  Co-ordinate rotation based low complexity 2D FastICA algorithm and architecture , 2010, The 2010 International Conference on Green Circuits and Systems.

[2]  D. Chakrabarti,et al.  A fast fixed - point algorithm for independent component analysis , 1997 .

[3]  Po-Lei Lee,et al.  Implementation of Pipelined FastICA on FPGA for Real-Time Blind Source Separation , 2008, IEEE Transactions on Neural Networks.

[4]  José Luis Lázaro,et al.  Novel HW Architecture Based on FPGAs Oriented to Solve the Eigen Problem , 2008, IEEE Transactions on Very Large Scale Integration (VLSI) Systems.

[5]  Christiaan Burger,et al.  Removal of EOG artefacts by combining wavelet neural network and independent component analysis , 2015, Biomed. Signal Process. Control..

[6]  Jack E. Volder The CORDIC Trigonometric Computing Technique , 1959, IRE Trans. Electron. Comput..

[7]  J. C. Majithia Nonrestoring binary division using a cellular array , 1970 .

[8]  Ganesh R. Naik,et al.  Online and automated reliable system design to remove blink and muscle artefact in EEG , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[9]  Chia-Hsiang Yang,et al.  An 81.6 $\mu {\rm W}$ FastICA Processor for Epileptic Seizure Detection , 2015, IEEE Transactions on Biomedical Circuits and Systems.

[10]  Kai Hwang,et al.  Computer arithmetic: Principles, architecture, and design , 1979 .

[11]  Bart Vanrumste,et al.  Validation of ICA as a tool to remove eye movement artifacts from EEG/ERP. , 2010, Psychophysiology.

[12]  Ganesh R. Naik,et al.  Low Complexity Single Channel ICA Architecture Design Methodology for Pervasive Healthcare Applications , 2016, 2016 IEEE International Workshop on Signal Processing Systems (SiPS).

[13]  Amit Acharyya,et al.  Coordinate Rotation Based Low Complexity N-D FastICA Algorithm and Architecture , 2011, IEEE Transactions on Signal Processing.

[14]  Lan-Da Van,et al.  Energy-Efficient FastICA Implementation for Biomedical Signal Separation , 2011, IEEE Transactions on Neural Networks.

[15]  Bashir M. Al-Hashimi,et al.  Hardware reduction methodology for 2-dimensional kurtotic fastica based on algorithmic analysis and architectural symmetry , 2009, 2009 IEEE Workshop on Signal Processing Systems.

[16]  P. Comon,et al.  Ica: a potential tool for bci systems , 2008, IEEE Signal Processing Magazine.