Abstract
This paper describes a VLSI implementation of the InfoMax algorithm for Independent Component Analysis in mixed-signal CMOS. Our design uses on-chip calibration techniques and local adaptation to compensate for the effect of device mismatch in the arithmetic modules and analog memory cells. We use our design to perform two-input blind source-separation on mixtures of audio signals, and on mixtures of EEG signals. Our hardware version of the algorithm successfully separates the signals with a resolution within less than 10% of a software implementation of the algorithm. The die area of the circuit is 0.016mm2 and its power consumption is 15μW in a 0.35μm CMOS process.
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References
Hyvärinen, A., Karhunen, J., Oja, E.: Independent Components Analysis, vol. 1. John Wiley and Sons, INC., New York (2001)
Comon, P.: ndependent Component Analysis - A New Concept? Signal Processing 3(36), 214–287 (1994)
Bell, A.J., Sejnowski, T.J.: The Independent Components of Natural Scenes are Edge Filters. Vision Research 37(23), 3327–3338 (1997)
Cardoso, J.: Infomax and Maximum Likelihood for Blind Source Separation. IEEE Signal Processing Letters 4, 112–114 (1997)
Lee, T.W., Girolami, M., Sejnowski, T.J.: Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Subgaussian and Supergaussian Sources. Neural Computation 11(2), 417–441 (1999)
Bell, A.J., Sejnowski, T.J.: An Information-Maximization Approach to Blind Separation and Blind Deconvolution. Neural Computation 7(6), 1129–1159 (1995)
Carvajal, G., Figueroa, M., Bridges, S.: Effects of Analog-VLSI Hardware on the Performance of the LMS Algorithm. In: International Conference on Artificial Neural Networks (ICANN), Athens, Greece, 10-14 September 2006, pp. 963–973 (2006)
Hairai, Y., Nishizawa, K.: Hardware Implementation of a PCA Learning Network by an Asynchronous PDM Digital Circuit. In: Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN), vol. 2(2), pp. 65–70 (2000)
Figueroa, M., Bridges, S., Diorio, C.: On-Chip Compensation of Device-Mismatch Effects in Analog VLSI Neural Networks. In: Advances in Neural Information Processing Systems 17. MIT Press, Cambridge (2005)
Carvajal, G., Valenzuela, W., Figueroa, M.: Subspace-Based Face Recognition in Analog VLSI. In: Platt, J., Koller, D., Singer, Y., Roweis, S. (eds.) Advances in Neural Information Processing Systems 20. MIT Press, Cambridge (2008)
Delorme, A., Makeig, S.: EEGLAB: An Open Source Toolbox for Analysis of Single-Trial EEG Dynamics Including Independent Component Analysis. Journal of Neuroscience Methods 134, 9–21 (2004)
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Valenzuela, W., Carvajal, G., Figueroa, M. (2008). Blind Source-Separation in Mixed-Signal VLSI Using the InfoMax Algorithm. In: Kůrková, V., Neruda, R., Koutník, J. (eds) Artificial Neural Networks - ICANN 2008. ICANN 2008. Lecture Notes in Computer Science, vol 5164. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87559-8_22
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DOI: https://doi.org/10.1007/978-3-540-87559-8_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87558-1
Online ISBN: 978-3-540-87559-8
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