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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F17%3APU122867" target="_blank" >RIV/00216305:26230/17:PU122867 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/s10710-016-9275-7" target="_blank" >http://dx.doi.org/10.1007/s10710-016-9275-7</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10710-016-9275-7" target="_blank" >10.1007/s10710-016-9275-7</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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

Název v anglickém jazyce

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

Popis výsledku anglicky

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

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GA14-04197S" target="_blank" >GA14-04197S: Pokročilé metody evolučního návrhu složitých číslicových obvodů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Genetic Programming and Evolvable Machines

ISSN

1389-2576

e-ISSN

1573-7632

Svazek periodika

18

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

38

Strana od-do

45-82

Kód UT WoS článku

000398142500004

EID výsledku v databázi Scopus

2-s2.0-84978628145