An Evolutionary Approach to Automatic Generation of VHDL Code for Low-Power Digital Filters
An evolutionary algorithm is used to design a finite impulse response digital filter with reduced power consumption. The proposed design approach combines genetic optimization and simulation methodology, to evaluate a multi-objective fitness function which includes both the suitability of the filter transfer function and the transition activity of digital blocks. The proper choice of fitness function and selection criteria allows the genetic algorithm to perform a better search within the design space, thus exploring possible solutions which are not considered in the conventional structured design methodology. Although the evolutionary process is not guaranteed to generate a filter fully compliant to specifications in every run, experimental evidence shows that, when specifications are met, evolved filters are much better than classical designs both in terms of power consumption and in terms of area, while maintaining the same performance.
KeywordsEvolutionary Algorithm Impulse Response Evolutionary Approach Automatic Generation Reduce Power Consumption
Unable to display preview. Download preview PDF.
- 2.Darwin, C.: On the Origin of Species by Means of Natural Selection. John Murray, London, UK (1859)Google Scholar
- 3.Drechsler, R.: Evolutionary Algorithms for VLSI CAD. Kluwer Academic Publishers, Dordrecht, The Netherlands (1998)Google Scholar
- 8.Jackson, L.B.: Digital Filters and Signal Processing. Kluwer Academic Publishers, Dordrecht, The Netherlands (1986)Google Scholar
- 9.Jackson, L.B.: Signals, Systems, and Transforms. Addison-Wesley, Reading, MA, USA (1991)Google Scholar
- 10.Zhao, Q., Tadokoro, Y.: A simple design of FIR filters with power-of-two coefficients. IEEE Trans. Circ. and Syst. 35 (1988) 556–570Google Scholar
- 11.Pirsch, P.: Architectures for Digital Signal Processing. John Wiley & Sons, Chichester, UK (1998)Google Scholar
- 12.Koza, J.R.: Genetic Programming: on the Programming of Computers by Means of Natural Selection. The MIT Press, Cambridge, MA, USA (1993)Google Scholar
- 13.Miller, J.F., Thomson, P.: Cartesian genetic programming. In Poli, R. et al. (Eds.), Genetic Programming European Conference (EuroGP 2000), Springer-Verlag, Berlin, Germany (2000) 121–132Google Scholar
- 15.The Mathworks, Inc.:, Signal Processing Toolbox. Natick, MA, USA (1983)Google Scholar