Switching optimisation in huffman code for power efficient data transmission

Different technologies have been emerged to address the issues of power consumption in digital communication. In CMOS technology, dynamic power accounts for 70%–90% of the total power dissipation and it largely depends on the representation of data and increases linearly with the switching activities (transition from logic level High to Low and vice versa). An efficient representation of data can minimise power consumption by reducing switching activities. In this paper, we have proposed an approach using genetic algorithm to optimise switching activities in the Huffman code for biological data compression. The performance of the approach has been evaluated by applying it to a set of biological datasets. The experiments yield that the proposed approach reduces the switching activity by 45.47% in the best case, by 36.45% in the average case, and by 16.42% in the worst case.