Design and Development of a Power Efficient Code for FIR Filter Coefficients Using Genetic Algorithms

Abstract

ABSTRACT With the explosive growth of wireless communication system and portable devices, the power reduction has become a major problem. In applications, such as personal communication systems, and portable storage devices, low power dissipation, hence longer battery life time is a must. With the rapid growth of internet and information on demand, handled wireless terminals are becoming increasingly popular. With limited energy in a reasonable size battery, minimum power dissipation in digital communication devices is necessary. Many of the communication system today utilize digital signal processors (DSP) to resolve the transmitted information. Finite impulse response (FIR) filters have been and continued to be important building blocks in many digital processing systems (DSP). Signal switching activity is a major component of power dissipation in CMOS circuits. Hamming distance is a measure of switching activity corresponding to the number of energy consuming transition in multiplier and accumulate (MAC) of filter while implementing on digital signal processors (DSP). The transition densities of multiplier input depend on the hamming distance between the successive filter coefficient values. For a multiplier the power is directly dependent on the transition densities and the probabilities of multiplier inputs. The hamming distance between consecutive coefficient values and the number of signal toggling in opposite directions thus forms the measure of bus power dissipation. Genetic algorithms can implemented as a computer simulation in which a population of abstract representations (called chromosomes or the genotype or the genome) of candidate solutions (called individuals, creatures, or phenotypes) to an optimization problem evolves toward better solutions. Traditionally, solutions are represented in binary as strings of 0s and 1s, but other encodings are also possible. The evolution usually starts from a population of randomly generated individuals and happens in generations. In each generation, the fitness of every individual in the population is evaluated, multiple individuals are stochastically selected from the current population (based on their fitness), and modified (recombined and possibly randomly mutated) to form a new population. The new population is then used in the next iteration of the algorithm. Commonly, the algorithm terminates when either a maximum number of generations has been produced, or a satisfactory fitness level has been reached for the population.