Call for Paper - July 2023 Edition
IJCA solicits original research papers for the July 2023 Edition. Last date of manuscript submission is June 20, 2023. Read More

A Ripple Carry Adder based Low Power Architecture of LMS Adaptive Filter

Print
PDF
IJCA Proceedings on National Conference on Information Processing and Remote Computing
© 2015 by IJCA Journal
NCIPRC 2015 - Number 1
Year of Publication: 2015
Authors:
A. S. Sneka Priyaa
C. Santhi

A.s.sneka Priyaa and C Santhi. Article: A Ripple Carry Adder based Low Power Architecture of LMS Adaptive Filter. IJCA Proceedings on National Conference on Information Processing and Remote Computing NCIPRC 2015(1):12-16, April 2015. Full text available. BibTeX

@article{key:article,
	author = {A.s.sneka Priyaa and C. Santhi},
	title = {Article: A Ripple Carry Adder based Low Power Architecture of LMS Adaptive Filter},
	journal = {IJCA Proceedings on National Conference on Information Processing and Remote Computing},
	year = {2015},
	volume = {NCIPRC 2015},
	number = {1},
	pages = {12-16},
	month = {April},
	note = {Full text available}
}

Abstract

The Least Mean Square Adaptive Filter is frequently encountered in wide variety of applications like signal processing, measurement and analysis of continuously changing parameters and signal analysis. The direct form of LMS Adaptive Filter does not support pipelining due to its recursive behavior. Thus modified Delayed LMS Adaptive Filter is preferred in which delays are decomposed so that pipelining is applied to power consuming blocks. Literature survey on the architectures of LMS Adaptive filter reveals that earlier works focused on the implementation using systolic architectures that gave rise to large adaptation delay. This arise a need for designing the LMS Adaptive filter with low adaptation delay. In this project, power efficient hardware architecture of modified delayed LMS Adaptive filter has been designed and implemented. The design of modified Delayed LMS Adaptive Filter is done after implementing pipelined architecture of Error Computation Block and Weight Update Block. The two important blocks involve the use of partial product generator and adder-tree structure which together perform the high-complexity operation. Adder-tree structure uses Carry-look ahead adder whose internal generate and propagate signals contribute to high power consumption. Hence further modification is made in the adder-tree structure by utilizing ripple-carry adder instead of carry-look ahead adder. This modification results in approximately 24% reduction in power relative to existing modified DLMS. Further power optimization is done by replacing adders with 4:2 compressors. The area complexity is also reduced as the number of required 4:2 compressors are less when compared to the requirement of adders. This modification results in 39% reduction in power relative to existing modified DLMS without degradation of steady-state-error performance. This implementation of power-optimized modified DLMS is done using Xilinx ISE Design Suite 14. 2.

References

  • B. Widrow and S. D. Stearns, Adaptive Signal Processing, Englewood Cliffs, NJ, USA: Prentice-Hall, 1985.
  • S. Haykin and B. Widrow, Least-Mean-Square Adaptive Filters. Hobo ken NJ, USA: Wiley, 2003.
  • M. D. Meyer and D. P. Agrawal, "A modular pipelined implementation of a delayed LMS transversal adaptive filter," in Proc. IEEE Int. Symp,Circuits Syst. , May 1990, pp. 1943–1946.
  • G. Long, F. Ling, and J. G. Proakis, "The LMS algorithm with delayed coefficient adaptation," IEEE Trans. Acoust. , Speech, Signal Process. ,vol. 37, no. 9, pp. 1397–1405, Sep. 1989.
  • G. Long, F. Ling, and J. G. Proakis, "Corrections to 'The LMS algorithm with delayed coefficient adaptation'," IEEE Trans. Signal Process. ,vol. 40, no. 1, pp. 230–232, Jan. 1992.
  • H. Herzberg and R. Haimi-Cohen, "A systolic array realization of an LMS adaptive filter and the effects of delayed adaptation," IEEE Trans. Signal Process. , vol. 40, no. 11, pp. 2799–2803, Nov. 1992.
  • M. D. Meyer and D. P. Agrawal, "A high sampling rate delayed LMS filter architecture," IEEE Trans. Circuits Syst. II, Analog Digital Signal Process. , vol. 40, no. 11, pp. 727–729, Nov. 1993.
  • S. Ramanathan and V. Visvanathan, "A systolic architecture for LMS adaptive filtering with minimal adaptation delay," in Proc. Int Conf. Very Large Scale Integr. (VLSI) Design, Jan. 1996,pp. 286–289.
  • Y. Yi, R. Woods, L. -K. Ting, and C. F. N. Cowan, "High speed FPGA-based implementations of delayed-LMS filters," J. Very Large Scale Integr. (VLSI) Signal Process. , vol. 39, nos. 1–2, pp. 113–131, Jan. 2005.
  • L. D. Van and W. S. Feng, "An efficient systolic architecture for the DLMS adaptive filter and its applications," IEEE Trans. Circuits Syst. II, Analog Digital Signal Process. , vol. 48, no. 4, pp. 359–366, Apr. 2001.
  • L. -K. Ting, R. Woods, and C. F. N. Cowan, "Virtex FPGA implementation of a pipelined adaptive LMS predictor for electronic support measures receivers," IEEE Trans. Very Large Scale Integr. (VLSI) Syst. , vol. 13, no. 1, pp. 86–99, Jan. 2005.
  • P. K. Meher and M. Maheshwari, "A high-speed FIR adaptive filter architecture using a modified delayed LMS algorithm," in Proc. IEEE Int. Symp. Circuits Syst. , May 2011, pp. 121–124.
  • P. K. Meher and S. Y. Park, "Low adaptation-delay LMS adaptive filter part-I: Introducing a novel multiplication cell," in Proc. IEEE Int. Midwest Symp. Circuits Syst. , Aug. 2011, pp. 1–4.
  • K. K. Parhi, VLSI Digital Signal Procesing Systems: Design and Implementation. New York, USA: Wiley, 1999.
  • C. Caraiscos and B. Liu, "A roundoff error analysis of the LMS adaptive algorithm," IEEE Trans. Acoust. , Speech, Signal Process. , vol. 32, no. 1, pp. 34–41, Feb. 1984.