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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
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)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
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
eBook Packages: Computer ScienceComputer Science (R0)