Trading between Quality and Non-functional Properties of Median Filter in Embedded Systems

Genetic programming, Genetic improvement, Cartesian genetic programming, Median function, Comparison network, Permutation principle, Median filter

Genetic improvement has been used to improve functional and non-functional properties of software. In this paper, we propose a new approach that applies a genetic programming (GP)-based genetic improvement to trade between functional and non-functional properties of existing software. The paper investigates possibilities and opportunities for improving non-functional parameters such as execution time, code size, or power consumption of median functions implemented using comparator networks. In general, it is impossible to improve non-functional parameters of the median function without accepting occasional errors in results because optimal implementations are available. In order to address this issue, we proposed a method providing suitable compromises between accuracy, execution time and power consumption. Traditionally, a randomly generated set of test vectors is employed so as to assess the quality of GP individuals. We demonstrated that such an approach may produce biased solutions if the test vectors are generated inappropriately. In order to measure the accuracy of determining a median value and avoid such a bias, we propose and formally analyze new quality metrics which are based on the positional error calculated using the permutation principle introduced in this paper. It is shown that the proposed method enables the discovery of solutions which show a significant improvement in execution time, power consumption, or size with respect to the accurate median function while keeping errors at a moderate level. Non-functional properties of the discovered solutions are estimated using data sets and validated by physical measurements on physical microcontrollers. The benefits of the evolved implementations are demonstrated on two real-world problems---sensor data processing and image processing. It is concluded that data processing software modules offer a great opportunity for genetic improvement. The results revealed that it is not even necessary to determine the median value exactly in many cases which helps to reduce power consumption or increase performance. The discovered implementations of accurate, as well as approximate median functions, are available as C functions for download and can be employed in a custom application (http://www.fit.vutbr.cz/research/groups/ehw/median).